Methods of improving the system of motivation of the university teaching staff

The purpose of the article is a systematic scientific study of the most optimal methods for motivating personnel of higher educational institutions and their improvement, taking into account the economic state of the country and the specifics of the processes of social transformation, which formed the basis for the development of methodological and practical recommendations. The main research methods were information retrieval methods, universal scientific methods, including methods of comparative and factor analysis, detailing, synthesis, generalization. This publication examines the methods of motivation and incentives for the teaching staff of universities, which are in demand for use in the transition to the forms of blended and elearning, along with the use of traditional approaches; a method for calculating the fund of surcharges, allowances and other incentive payments to the teaching staff is proposed, Аннотация Целью статьи является системное научное исследование наиболее оптимальных методов мотивации персонала высших учебных заведений и его совершенствование с учетом экономического состояния страны и специфики процессов общественной трансформации, и на этой основе разработка методических и практических рекомендаций. Основными методами исследования стали методы поиска информации, универсальные научные методы, в том числе методы сравнительного и факторного анализа, детализации, синтеза, обобщения. В данной публикации рассмотрены методы мотивации и стимулирования профессорскопреподавательского состава (ППС) вузов, востребованные к применению при переходе к формам смешанного и электронного обучения наряду с использованием традиционных подходов; предложена методика расчета фонда доплат, надбавок и 120 Candidate of Economic Sciences, associate Professor of Economics, Don State Technical University Rostov-on-Don, Russia. 121 Candidate of Economic Sciences, associate Professor of Economics, Don State Technical University Rostov-on-Don, Russia. 122 Candidate of Economic Sciences, associate Professor of Economics, Don State Technical University Rostov-on-Don, Russia. 123 Candidate of Economic Sciences, associate Professor of Economics, Don State Technical University Rostov-on-Don, Russia. Kuzubov, A.A., Maksimenko, A.N., Malinina, O.Y., Fedorkova, A.V. / Volume 10 Issue 48: 274-287 / December, 2021 Volume 10 Issue 48 / December 2021 275 https:// www.amazoniainvestiga.info ISSN 2322 6307 reflecting the relationship and mutual influence of workload, remuneration and surcharges, an objective assessment of work with the volume of work actually performed, overtime work, scientific work, the introduction of modern methods and technologies of teaching allows you to optimally distribute funds to motivate the teaching staff. The materials of the article will be of interest to scientific and practical workers, graduate students and students.

Abstract
The purpose of the article is a systematic scientific study of the most optimal methods for motivating personnel of higher educational institutions and their improvement, taking into account the economic state of the country and the specifics of the processes of social transformation, which formed the basis for the development of methodological and practical recommendations.
The main research methods were information retrieval methods, universal scientific methods, including methods of comparative and factor analysis, detailing, synthesis, generalization. This publication examines the methods of motivation and incentives for the teaching staff of universities, which are in demand for use in the transition to the forms of blended and elearning, along with the use of traditional approaches; a method for calculating the fund of surcharges, allowances and other incentive payments to the teaching staff is proposed,

Introduction
One of the most important, generally recognized and unsolved socio-economic problems of modern Russia is the crisis of social and labor relations, which manifests itself in: − underestimation of the highly qualified workforce; − depreciation of labor values; − loss of meaningfulness of the function of labor; − transformation of labor from the basis of a way of life into a means of survival.
Unfortunately, this crisis is noted in many spheres and sectors of the economy, especially in universities.
The labor of university workers is one of the specific types of labor activity, which, directly or indirectly, is a necessary and obligatory component of social labor, directly related to all mechanisms of social reproduction.
Its main feature is the provision of knowledge, direct and indirect improvement of the qualifications of the labor force, that is, an increase in the efficiency of living labor.
After all, it is higher education that serves as one of the sources of the country's economic growth, making the work of each individual more productive, increasing his ability to perceive and use in practice new methods of production and management, makes it possible to manage technical means, contributes to the formation and development of scientific ideas in order to achieve the best. managing existing resources and increasing labor productivity.
The above is of particular importance in connection with the urgent need to orient the activities of the university towards achieving the results of its activities of the systemic property of emergence, which in the Higher School is considered as education, that is, an organic combination of the processes of teaching, upbringing and personal development.
Therefore, special attention of the heads of universities should be focused precisely on the effective use of personnel by stimulating a motivational orientation in their work, that is, on encouraging employees to productive activity (motivation is the main element of activating the human factor, which not only creates material prerequisites for production, but also provides them effective functioning).
The search for approaches to the improvement and formation of an effective system of labor motivation for teaching staff determines the implementation of the research and determines the relevance of the topic of work.

Theoretical Framework or Literature Review
In general, motivation is the process of encouraging oneself and others to participate in certain activities aimed at achieving personal or organizational goals. The key link in motivation has been known for a long time and has the following form: needs-motivation-reward (Zhukovskaya, 2018).
American scientist D. Atkinson was one of the first to propose a general theory of motivation. He noted that the strength of a person's desire to achieve a set goal can be reflected by the following formula (Eren & Yeşilburs, 2019, Dagsvik et al, 2021: S -the power of motivation (striving); PP -the power of the motive for achieving success as a personal predisposition; SE -subjectively estimated probability of achieving the set goal; VT -the value of achieving this goal (task) for a person.
Any company or institution, including a university, has a major challenge to try to improve performance by motivating its employees.
Thus, we can say that each company tries to maximize the result of its activities, but each employee wants to get the result of his work in the form of a decent wage.
In practice, a result-oriented motivational model is often used, the so-called Blake-Mouton "Management grid" (Thommen et al, 2021). This is a kind of matrix formed by the cross section of two components: care for work and care for people, the analysis of the content of this model showed that: a) satisfactory performance is maintained through the constant search for compromises between production requirements and the individual needs of workers; b) satisfactory performance of work may be unattainable, because, on the one hand, employees are lazy, indifferent and not interested, and on the other hand, it is impossible to establish satisfactory social relations between them; c) high labor productivity is the result of an optimal balance between production requirements and the needs of individual employees; d) satisfaction of social needs leads employees to a relaxed friendly atmosphere of work and, accordingly, to a calm rhythm of work.
As evidenced by the results of the studies presented in the work (Ledneva & Ivanova-Shvets, 2018), if there were sufficient means of subsistence, 20% of people would not work at all under any circumstances.
From the rest: − 36% are ready to work if the work is interesting; − 36% -to avoid boredom and loneliness; − 14% -for fear of losing themselves; − 9% -because work brings joy.
Only 12% of surveyed employees consider money to be the main motive of their activities, 45% prefer fame; 35% -satisfaction with the content of the job; 15% -the power that work gives.
Adams Scott wrote: "The reward system is an important tool for keeping income low, but the world has not yet come up with a better personnel management mechanism than methods of material incentives, methods of material reward. New approaches to incentives involve the abandonment of traditional hourly systems or pay based on the complexity coefficient, etc., as well as their replacement with wages, which consists of two components: a base rate and additional incentive payments depending on individual results and / or efficiency. divisions / companies " (Adams, 2002).
J. Adams proposed "The Theory of Justice (Equality)". According to this theory, workers subjectively determine the ratio of the remuneration received and the expended effort, and then compare it with the remuneration of other people for similar work. Only after receiving a fair remuneration will a person work with full dedication.
Usually, workers are dissatisfied not so much with the size of their wages as with its unfairness, the lack of its connection with the results of their labor, the chaos in the ratio of the reduction in the wages of specialists from different departments, and a sharp difference in the remuneration of homogeneous specialists at enterprises of the same region. Hence the turnover of personnel, the instability of teams, the loss of an enterprise in training new personnel, and the like (Stasyuk, 2020, Bezuglova, 2020. Consequently, the employee motivation system should cover material and non-material remuneration in accordance with the specifics of the employee's activities. F. Herzberg proposed a two-factor model for determining motivational strength (MS): where; HFі -the level of dissatisfaction with the i-th hygienic factor. One of these factors is the presence or absence of wages; SFј -quantitative assessment of satisfaction of the j-th need; n -is the number of hygienic factors; m -is the number of motivating factors.  .
In this model, the so-called hygienic factors of behavior motivation and pure ones are distinguished.
The latter are called motivators. Therefore, the task is to find, on the one hand, factors that lead to job satisfaction, that is, are motivators, and on the other hand, hygienic factors that cause discontent and act negatively.
Motivational factors include the following: − work-related successes; − recognition of the employee as a person; − the content of labor activity; − responsibility for the assigned direction of work; − career opportunities.
The hygienic factors of behavior motivation include: − general principles of professional activity; − professional leadership qualities; − the salary; − interpersonal relationships; − constructive working conditions.
The conducted analytical studies allow us to conclude that the motivation and stimulation of the activities of university teachers are reduced mainly to material and monetary remuneration, and in management terms they represent the manipulation of various forms and methods of remuneration.
This relationship is focused on the form of remuneration and only secondarily on its quantitative characteristics.
Moreover, the latter are very often a kind of constants that are set by a combination of factors, including: wage rate, average wage, available resources, etc.
The quantitative components of wages in the form-quantity dyad are less volatile than those that make up incentive controls.
Consequently, effective incentives for the teaching staff at the university can be achieved only if the following conditions are met: labor rationing and decent wages, time maneuvering and wage differentiation.

Methodology
The analysis of the current state and prospects for the development of personnel in the field of education will be presented in the form of estimated predicted values predicted of the future number of people employed in the field of education in the Russian Federation using the forecasting method based on the average growth rate of row.
In the research, the authors used official statistics on the number of people employed in the education sector in the Russian Federation. On the graph, we present the number of people employed in education at the level of the Russian Federation. Analysis of the graph of the dynamics of the number allowed us to conclude that the time series does not have a sufficiently pronounced development trend, therefore, the forecast of the number of people employed in the field of education in the Russian Federation can be determined by the average growth rate of a row.
The average growth rate of a row is calculated by the formula: Next, you need to calculate the forecast error. Possible forecast error is calculated by the formula: (4) tnormalized deviation, which is determined by the selected confidence level; nnumber of known levels of the series; σ -the variance of the predicted market element, which can be calculated by the formula (5) The predicted value of a market element is determined as an interval: (6) To calculate the variance and forecast error, we will draw up a table.
The graph shows the change in the number of people employed in the field of education in the Russian Federation, taking into account the calculated forecast. A decrease in the number of employees can lead to a decrease in overall productivity in this area. In this regard, it is necessary to improve the system of labor motivation and the calculation of the fund for allowances, additional payments and benefits.

Results and Discussion
Modern management structures of higher education institutions are complex systems, the effectiveness of which depends significantly on the quality of management.
To achieve a high quality of university management in the context of ongoing educational reforms, the leader no longer lacks personal experience, intuition and organizational skills.
This situation is due to the fact that in the formation of strategic goals and the trajectory of decision-making, the leader is forced to rely on complex evaluation criteria to achieve the final results.
Consequently, the methodological basis of the concept of university management should be not only a set of tools, methods and models for decision-making, but also modern methods of motivating the work of the teaching staff and staff (Chudnova, 2019, Denisenko et al, 2021.
The motivation for the activity of a university teacher is in many ways different from receiving payment for the provision of ordinary services.
A very important incentive for him (and this is confirmed by the data of sociological research) is the professional recognition of colleagues who are able to assess his intellectual potential and scientific competence.
This serves both as a condition for maintaining the autonomy of the teaching profession and as a means of internal control and self-regulation.
The result of the attestation of a university teacher is an academic degree and an academic title awarded by the scientific community of professionals in this specialty (Nikulina, 2008).
That is, it is impossible to achieve the set goals with people devoid of motivation.
In our opinion, the factors influencing the motivation of university staff, which should attract, stimulate people and who should exist in the atmosphere of the university, so that work in it attracts not only because of the salary, can be presented in the form of table 3. It follows from this that for effective management of a university it is necessary to develop strategic management of motivation, containing a knowledge base about goals, methods of motivation and ways to achieve them. Strategic management is a set of actions and procedures for the formation of strategies, their assessment and operational management of the chosen strategy.
Thus, strategic management is the main goal of development, which necessitates the solution of a number of basic interdependent tasks, which include: − improving the management mechanism of the teaching staff; − development of methodological foundations for organizing a labor motivation management system; − selection of the main criteria and indicators of management efficiency in the context of education reform.
Since the objects of management are people, then, taking into account the specifics and complexity of university personnel management, it can be argued that further development and increase of motivation will take place not within the framework of one management theory, but at the junction of psychology (within the framework of studying the typology of personality behavior), sociology (in the study of public opinion and social motives), jurisprudence (within the framework of legal support of the interests of the individual), economics (within the framework of stimulating labor behavior) (Hou et al, 2021;Hitchcock et al, 2021).
In addition, the analysis carried out by the authors convincingly indicates that the development and application of modern flexible concepts of motivation is based on a rational principle of personnel behavior, which assumes that the management system should ensure mutual coordination of the interests of the individual and the individual. the university, in fact, must meet the new market requirements.
The results of the university's activities largely depend on the resources available and the efficiency of their use. In turn, effective management is determined by the attitude of personnel to work, forms and methods of motivation (Kuzubov, 2018).
As a result of the analysis of the known concepts of labor motivation in accordance with the problem under study, a generalizing interpretation of the concept of "labor motivation" is proposed, meaning the process of purposeful application of a certain set of diverse incentives to activity and regulation of labor relations to achieve the goals of the organization by coinciding its interests and the interests of the employee.
Under the conditions of a university, the organization of effective functioning is carried out on the basis of a hierarchical structure, in which information about the required level of quality of training of students is transferred from the control subsystem to the controlled one. Information about the results achieved is transmitted through feedback channels to the control subsystem. As a result, the interest of both subsystems in improving the quality of the educational process is ensured.
At the same time, the motivation of personnel labor is of great importance, in which two main forms can be distinguished from the point of view of scientific performance and from the point of view of scientific status. The scientific results of employees are assessed on the basis of scientific works of students, reports at conferences, monographs, etc. The scientific status is assessed in accordance with academic degrees, rank, membership in scientific societies and the position held, the creation of scientific schools, the frequency of citation in domestic and foreign publications (Voronina, 2019;Ivaniuk, 2021).
Despite the dubious validity of the assessment of personnel based on specific results, this form, without taking into account the scientific status, does not ensure the effective functioning of the university in a sufficiently long term, especially in the presence of uncertainty about the influence of the external environment (Absolon, 2019).
It follows from this that the university should be highly dynamic due to the formation of new relations, growing competition, political instability and socio-economic processes.
Moreover, as a result of the transition of the country's economy to the market, new tasks have arisen for the education system. Financial problems are particularly acute, forcing decisive action in cost adjustments and management practices.
The system of labor motivation, social guarantees and social protection is a priority in the concept of personnel policy. Reforms will not be effective without changing the wages of intellectual labor. No hiring policy can matter in hiring if it consists of unsecured income (Dagsvik, 2021; In this regard, it is necessary to create an effective remuneration system in universities, which would allow, firstly, to use more efficiently the already insufficient and limited budgetary funds, secondly, to optimally distribute funds between university departments, and thirdly, to attract additional sources of financing for employees' remuneration.
The authors propose the calculation of the wage fund required for the payment of benefits to teachers of higher educational institutions.
The monthly salary fund for the payment of bonuses for the position held is determined by the formula: The amount of the allowance for an academic degree must be established by law.
− five times the minimum wage established by law for a doctoral degree; − three times the minimum wage established by law for a candidate of sciences.
To pay bonuses for a scientific degree, a monthly salary fund is required: MPFAD = 5 * MW * (N1) + 3 * MW *(N2), (10) MPFAD -monthly salary fund for the payment of awards for an academic degree; MW -minimum wage; N1 is the number of full-time positions of doctors of sciences; N2 -the number of staff units of candidates of sciences; In addition to defending candidate and doctoral dissertations, stimulation of research work is proposed through lump-sum payments in the form of bonuses, or additional payments to the salary for the current year, depending on the financial capabilities of the university for such activities: − Premium for the release of a monograph, determined by the formula: PM = Kc * S, where (Kc is the number of pages, S is the amount of funds allocated by the university to pay for the 1st published page of the text); − Premium for posing a significant problem, determined by the formula: PSP = ES * 3, where (ES is the salary of one university employee); − Premium for the introduction of a new scientific method, determined by the formula: PNM = ES * 4, where (ES is the salary of one university employee); − Premium for the creation of a new theory, determined by the formula: PNT = ES * 10, where (ES is the salary of one university employee); − Premium for the implementation of a new theory, determined by the formula: PINT = ES * 5, where (ES is the salary of one university employee); − Premium for the creation of a new scientific school, which is determined by the formula: PNSS = ES * 8, where (ES is the salary of one university employee); − Premium for the training of one candidate of sciences, which is determined by the formula: PCS = ES * 7, where (ES is the salary of one university employee); This list of premium can be supplemented depending on the specifics of the university.
Thus, the monthly salary fund of the teaching staff is:
In addition, the hourly wage (P) is 10% of the monthly wage fund, without additional payments to the teaching staff.
With extra-budgetary funding, the procedure for the formation and cost of the wage fund is changed. The sources for the creation of an off-budget fund can be: − funds received for training, retraining and advanced training of specialists on a commercial basis; − funds from the lease of fixed assets and land plots; − proceeds from the sale of own equipment and property, educational, methodological and scientific products; funds from the provision of additional educational and intermediary services; funds from the provision of paid services to the population; voluntary contributions from legal entities and individuals.
Extrabudgetary funds based on estimates can be spent on: − development of scientific research; − remuneration of employees; − to establish incentive allowances, allowances and bonuses − major and current repairs of buildings, structures and equipment; − payment of utility and operating costs; − material and technical support of departments; − social payments; − traveling expenses; − payment of expenses for professional development of employees; − publishing activities; − for the payment of official salaries to the staff of departments, which are fully financed from extrabudgetary funds that are created at the university by the decision of the Academic Council; − to pay for additional work that is not part of the direct responsibilities of an employee of this unit; − for the remuneration of freelance employees (including students).
Payments of this nature are made in the manner prescribed by the university.
At the University Academic Council, taking into account the financial condition of the university, the main directions of spending extra-budgetary funds from the centralized fund of the university are determined, which is approved by the order of the rector.
Within the limits of the amounts allocated for wages, based on the approved budget for the universities, the heads of departments should be involved in determining the forms and amount of remuneration for their subordinates.
In our opinion, the optimal distribution of funds for paid education in universities should be: 51% -in the funds of universities, 49% -in the funds of dean's offices. This 49% can be distributed between faculties, depending on the number of students studying on a paid basis in these dean's offices. At least a third or even a quarter of these funds can give a positive result. Delegation of functions to the heads of departments (directors of institutes, deans, heads of departments) for the management of these funds made it possible to motivate employees who ensure high-quality work of those teachers who effectively carry out their work, introduce new methods, work on candidate and doctoral dissertations, etc. which will certainly be reflected on the quality of student education and will have a positive effect on such a nationwide problem as corruption in the university.
In addition to optimizing the distribution of funds within the university, the described methodology includes a mechanism of responsibility of the heads of both the highest echelon of power of universities and structural units for the budget funding allocated to them, which distinguishes it favorably from the wellknown and promotes the promotion of universities in effective use with limited financial resources in the field of education.
The next, in our opinion, direction of increasing the motivation of the system of training university personnel should be the establishment of a relationship between the amount of remuneration and the level of implementation of modern teaching technologies, for example, distance learning systems, computerized educational systems, and so on.
Many universities are already working on the design, development and delivery of innovative training courses based on computer and telecommunication technologies, employing entire teams, "teams" of teachers of various technologists, organizers and profiles.
In traditional teaching, the teacher's contribution is calculated based on the workload of the hours that he spends on lectures, seminars, tests and exams, consultations, etc. -the number of contact hours.
How to calculate the teacher load in innovative teaching when audio, video, radio, television and electronic lectures, teleconferences and other teaching methods are actively used?
The authors analyze the characteristics of approaches to determining the contribution of teachers to traditional and innovative learning (Table 4). (1) Increase the number of teaching hours (2) increasing the number of training hours when fixed cost of the course and fixed learning outcomes ("business approach") 1. Consider an approach to determining teacher remuneration based on teaching hours.
The key metric here is the number of hours of teaching using learning modules by those learners that the teacher has been involved in creating or using.
A training module is a relatively independent part of a training course, in which educational goals are achieved by presenting educational material based on a certain information technology (their combination) and an appropriate set of pedagogical methods and techniques.
Examples of training modules: − watching a video lecture; − the study of the subject and the implementation of self-control using a computer training program; − consultations with the lecturer by e-mail; − participation in a training video conference; − obtaining specific statistical, experimental and other data from interactive databases; − familiarization with the works of teachers on the pages of the World Wide Web of other educational institutions, etc.
In this case, the teacher's salary will be: T = (Li x ki x ti) x h (12) T is the teacher's salary; Li is the number of training hours based on the use of the i-th training module (i=1,... n), , created or applied with the participation of this teacher on the basis of a certain information technology (or their combination) Li = lij, lij -is the number hours of training for the j-th student (j=1,...n) using the i-th training module; ki is the coefficient of the teacher's participation in the creation or application of the i-th (i=1,... n) training module; ti is the coefficient of labor intensity or creation of an application for a training module based on this educational technology; h -the cost of 1 hour of study at an educational institution.
The teacher maximizes the salary by increasing the number of students' teaching hours.
1. Using a "business approach" to the definition of remuneration, the teacher is looking for the fastest and most effective way to achieve the desired learning outcome for a fixed cost of the course. That is, the following statement holds: write down what you need to learn and minimize the time it takes to do this. 2. Thus, the teacher will strive for the fastest and most effective way to achieve the desired learning outcome at a fixed cost of the training course: T = ki x (Si -ei x qi) where: T is the size of the teacher's salary; ki is the coefficient of the teacher's participation in the creation or application of the i-th training module (i = 1,...m), the Si -cost of the i-th training module (i = 1,...m), is included in the cost of the training courseS (S = Si); ei is the average hourly costs for equipment, rental of communication channels, software, administrative overheads and other costs not related to labor costs for the creation and use of the i-th training module in this educational institution; qi is the number of training hours in the i-th training module.
Thus, in our opinion, the procedure for assessing the teaching staff should include the following main criteria: teaching skills; scientific publications; scientific prizes and awards; introduction of new teaching methods; rating in the monthly survey of students; the degree of workload; participation in international scientific conferences; scientific work abroad; preparation of student research papers for the Olympiads; availability of graduate students; grants; patents, inventions; access to practical developments and the like.


Creative dance associated with traditional Portuguese singing as a strategy for active aging: a comparative cross-sectional study

Background Multimodal forms of exercise can influence several physical and mental factors important for successful aging. In the present study, we introduce a new type of multimodal intervention, combining movement (creative dance) with traditional singing. This study aims to compare physical fitness, functional physical independence, depressive symptoms, general cognitive status, and daytime sleepiness among older adults participating in multimodal exercise, those participating in traditional physical exercise, and those not actively engaged in physical exercise. Methods This cross-sectional study included 112 people aged ≥ 65 years (75.3 ± 0.7 years) living independently in the community, divided into 3 groups: multimodal exercise (n = 34), traditional exercise (n = 41), and no physical exercise (n = 37). Results The multimodal exercise group showed greater flexibility of the lower limbs and upper limbs/shoulders and better general cognitive status than the traditional exercise group (p < 0.05). The traditional exercise group had better agility and dynamic balance, aerobic endurance, and strength of the lower and upper limbs than the no-exercise group (p < 0.05). Conclusions The results suggest that the two types of programs studied may have different impacts on some of the variables investigated and support the design of future experimental studies that include interventions based on the combination of creative dance and traditional Portuguese singing.

changes in the quality of life of older people [6,7]. Physical exercise is also associated with improvements in the cognitive, social, and emotional domains [8,9]. Several psychological aspects benefit from exercise participation, including mood states, depression, anger, confusion, vitality, stress, self-esteem, self-concept, depression, and anxiety [10]. Furthermore, exercise can also facilitate social interactions and prevent isolation among older people [11].
Dance is a specific form of exercise that occurs in pleasant and energetic environments [12,13] and contributes to successful aging [12,14]. Dance can improve motor skills, balance, coordination, aerobic endurance, social relationships, and positive emotions [12,[14][15][16][17]. In particular, creative dance has been shown to improve proprioception [18], strength, aerobic endurance, flexibility, agility and dynamic balance, body composition, and life satisfaction [19]. Recently, a creative dance program was effective in improving the strength, flexibility, functional balance, and mobility of older adults [12].
Participation in artistic and cultural activities has also been used to promote the well-being, quality of life, and health of older people [20,21]. Among these activities, singing has attracted considerable interest in the scientific community. There is evidence of its positive effects on personal and social well-being, cognitive functioning, and physiological health [20,22]. A study with older people belonging to a choral singing group showed benefits of singing in three quality of life domains (psychological, social relationships, and environment), regardless of age and the presence of depressive symptoms [23]. A qualitative study also reported that singing activities could have a positive influence on social, emotional, physical, and cognitive domains irrespective of age, gender, nationality, or well-being status [24].
The present article introduces a novel type of multimodal exercise, combining movement (creative dance) with traditional singing. The singing component of the intervention is based on "Cante Alentejano", a traditional polyphonic singing endogenous to the Alentejo region (Portuguese region where the current study was developed), in which the lyrics explore rural life themes [25]. "Cante Alentejano" is part of the Intangible Cultural Heritage of Humanity of United Nations Educational, Scientific and Cultural Organization (UNESCO). For comparion, we include another type of exercise in the present study, which we called "traditional exercise" as it includes common features of exercise for older adults, namely, aerobic, strength, balance, agility, and range-ofmotion exercises.
Furthermore, in the present study, we consider several health and functional indicators that influence older adults' quality of life, namely, functional physical fitness, functional physical independence, depressive symptoms, general cognitive status, and daytime sleepiness. This study aims to compare these health indicators among older adults participating in multimodal exercise (creative dance combined with traditional Portuguese singing), those participating in traditional physical exercise, and those not actively engaged in physical exercise.

Study design and participants
This comparative cross-sectional analytical study included older adults ≥ 65 years of age (75.3 ± 0.7 years) living independently in the community. Participants were recruited in Évora and Beja districts (Portugal) through a direct invitation in social centers, pensioners' associations, senior universities, and parish centers.
The inclusion criteria were as follows: ≥ 65 years of age, absence of cognitive impairment [> 24 points on the Folstein Mini-Mental State Examination (MMSE)] [26] and the ability to autonomously carry out the exercise sessions and physical evaluations. For the participants involved in physical exercise programs, only those who performed traditional exercise or multimodal exercise (creative dance combined with traditional Portuguese singing) twice a week for at least six months were considered for the present study. The participants included in the no-exercise group could not have been engaged in any physical exercise program in the last six months before data collection. In total, a sample of 112 older people was assembled and distributed as follows ( Fig. 1): traditional exercise group (TEG) with 41 participants (77.8 ± 1.2 years old), multimodal exercise group (MEG) with 34 participants (74.1 ± 1.1 years of age), and no-exercise group (NEG) with 37 participants (73.9 ± 1.1 years of age). This study was approved by the University of Évora Ethics Committee for research in human health and wellbeing (reference number 16-012), having followed the Declaration of Helsinki. All participants gave their written consent to participate in this study.

Procedures
Between April 2017 and January 2018, the evaluations took place in appropriate rooms for physical exercise practice (Beja and Évora). The tests were applied under the same conditions. Two raters were trained to apply the tests, which revealed interrater reliability between 0.722 and 0.999 as assessed through Pearson and Spearman's bivariate correlation coefficients.
The participants were given instructions to perform the tests, reinforced with demonstrations, and asked to do their best in each test. Additionally, individual questionnaires were administered to all participants in a calm and safe environment in the form of an interview. This questionnaire was used to collect sociodemographic information.

Anthropometry and physical activity levels
The participants were weighed on an appropriate scale (Seca, Hamburg, Germany) to the nearest 0.1 kg, wearing minimal clothing and barefooted. Height was measured to the nearest 0.1 cm, according to standardized procedures [27] with a scale attached to a stadiometer (Seca, Hamburgo, Alemanha). The body mass index (kg/m 2 ) was calculated from the weight (kg) divided by the height (m) squared.
Waist circumference was measured according to the World Health Organization protocol [28] with anthropometric tape (Seca, Hamburg, Germany). The average of two measurements was calculated, and if they differed by more than 1.0 cm, a third measurement was made, averaging the two closest measurements.
Habitual physical activity levels and sedentary behavior were evaluated to quantify a possible interference of physical activity in the study's dependent variables and measured with the International Physical Activity Questionnaire [29]. The total metabolic expenditure (MET-min/week) was calculated by determining the time (min/day) and frequency (days/week) spent in each of these activities.

Functional physical fitness and balance
Functional fitness included the assessment of agility and dynamic balance (time in seconds in the 8-foot up-andgo test), lower limb flexibility (distance in centimeters in the chair sit-and-reach test), upper limb/shoulder flexibility (distance in centimeters in the back scratch test), lower body strength (number of repetitions in the chair stand test), upper limb strength (number of repetitions in the arm curl test) and aerobic endurance (distance covered in meters in the 6-min walk test) of the Senior Fitness Test battery [30]. Dynamic and static balance was assessed using the Fullerton Advanced Balance scale [31], composed of ten tasks rated on an ordinal scale between 0 (worst) and 4 points (best). The sum of the points obtained in each of the ten tasks corresponded to the final grade in the multidimensional balance measure (0-40 points).

Physical and functional independence, depressive symptoms, general cognitive status and daytime sleepiness
Physical and functional independence was measured using the Composite Physical Function scale [32], which includes 12 daily activities divided into ten instrumental activities (related to community tasks, such as housework or commuting) and two basic activities (related to personal functioning, such as hygiene and nutrition). The evaluation of depressive symptoms was carried out using the Portuguese version of the Geriatric Depression Scale [33,34]. General cognitive status was assessed using the MMSE [26,35], with an internal structure of 20 individual tests assessing the nomination, repetition, three-step command, reading, writing a sentence, copying intersecting pentagons, orientation, attention/concentration/ calculation, memory and recall domains. The Epworth Sleepiness Scale (ESS) [36] was used to assess daytime sleepiness, a scale consisting of eight items, where participants were asked to rate the probability of falling asleep in eight different daily life situations on an increasing scale (0-3 points).

Exercise programs
Both exercise programs (MEG and TEG) were carried out by specialized and accredited instructors (graduated in physical education or sports sciences). The physical exercise session instructors were blind to the study objectives and had not participated in the data collection or analysis. Exercise program uniformity was guaranteed for each group through the joint planning of the exercise sessions. The participants included in the TEG and MEG were involved in the respective exercise program for more than six months, with two weekly sessions lasting 50 min each.
The exercise program for the TEG focused on improving motor and coordination skills to enhance functional performance. Exercise sessions started with a warm-up period (approximately 10 min) that included light mobility exercises for cardiorespiratory and neuromuscular activation. The exercises consisted of walking in different directions with changes in the speed of movement and range of stride length and simple games of movement that involved touching, catching, throwing, or swinging. Dynamic muscle stretching and joint mobilization exercises were also performed. With a duration of 35 min, the main part of the session involved the stimulation of various physical capacities (flexibility, agility, dynamic balance, strength, and aerobic endurance) that alternated across in six mandatory stations. At each station (approximately 5 participants per station), the exercises were aimed to be adapted and individualized at an intensity based on the participant's characteristics for 3 min, with 2 min of active recovery between rotations. In the last 5 min, a return to rest was accomplished by breathing exercises and stretching the main muscle groups with the aim to reduce muscle and joint tension. The intensity of the tasks was gradually increased throughout the exercise program, while considering each participant's individuality and specificity.
The MEG program sessions started with a warm-up period (10 min) involving a slight, low-pitched vocal warm-up (inhalation, exhalation, yawning movement, lip, and tongue vibration). Simultaneously, the participants performed simple movements with different degrees of balance, coordination, and flexibility, while exploring the free space, following the instructors' indications (e.g., static stretching movements, creative dance games, walking forward, backward, to the right or left, slow/ fast gait, alternating steps with hands-on-hips, alternating steps and leg flexion-extension with hands-on-hips, lifting the knees, and arms extension-flexion), and synchronizing the expressive movements of creative dance with traditional Portuguese singing. In the main part of the session, with a duration of 35 min, the participants worked in groups (organized in different spatial formations such as circle, quadrille, and columns). In this phase of the session, the participants simultaneously sang traditional Portuguese music and explored the expressive dance movements, while following the instructor's visual demonstration. Although there was imitation work, the participants were encouraged to explore their movement from the basic elements of movement: body (body parts), space (formations, directions, levels, trajectories), time (slow/fast/pause), dynamics (strong/weak/straight/ wavy) and interrelationships (pairs and in groups). Songs such as "ceifeira", "monda", "ciranda", "pastor", "ó rama da oliveira", "parreira", "romã", "ribeira vai cheia", and "gotinha de água", among others, were examples of the type of singing that was used, and the lyrics of the songs were used as a stimulus for creating movement. The activities carried out in the sessions evolved from simple and light tasks to more complex and moderate tasks. At the end of the session (5 min), some muscle stretching exercises were performed, working on relaxation, especially breathing exercises.

Data analysis
Since the participants' demographic and anthropometric variables did not follow a normal distribution, differences between groups were analyzed using Mann-Whitney U tests. The comparisons among the three groups were made through successive covariance analyses (ANCOVA) with adjustments for each group's average age and sex proportions.
Additionally, Cohen's d was calculated for the variables under study, with 90% confidence intervals to measure the magnitude of the differences between the groups. The effect size limits were considered as follows: no effect (d < 0.2), small effect (0.2 ≤ d < 5), medium effect (≤ 0.5 d < 0.8) or large effect (d ≥ 0.8) [37]. The significance level of p < 0.05 was used for all statistical tests performed. Data analysis was performed using SPSS Statistics software (version 22.0 for Windows; IBM), and the effect sizes were calculated online (http:// www. socsc istat istics. com/ effec tsize/ Defau lt3. aspx).

Results
The participant sociodemographic profile is presented in Table 1. There were no significant differences in weight, body mass index, or abdominal circumference among the participants in the three groups. Regarding participant height, the results revealed significant differences between the NEG and MEG (p = 0.013) and between the NEG and TEG (p = 0.031). Physical activity levels did not significantly differ among the groups. Figure 2 shows the comparative results between the NEG and TEG, with adjustments for the average age of each group. The NEG participants showed less agility and dynamic balance than the TEG participants (p = 0.001; large effect: d = -0.94). The TEG participants also had higher strength levels in the lower body and upper limbs (p = 0.003, large effect: d = 0.86; p = 0.000, large effect: d = 1.07, respectively) and greater aerobic endurance (p = 0.000; large effect: d = 1.08).  . 2 The comparison between the NEG and the TEG, with adjustment to each group average age and sex proportions Comparing the NEG with the MEG (Fig. 3), maintaining the adjustment for the average age of each group, the MEG participants showed greater agility and dynamic balance than the NEG participants (p = 0.024; average effect: d = -0.74), greater flexibility of the lower limbs (p = 0.010) and a more favorable general cognitive status (p = 0.020), despite a small effect for the latter two (d = -0.27; d = -0.32, respectively). Figure 4 shows the comparative results between the two groups involving physical exercise (TEG and MEG) with adjustments for the average age of each group. The MEG participants showed greater flexibility of the lower Fig. 3 The comparison between the NEG and the MEG, with adjustment to each group average age and sex proportions Fig. 4 The comparison between the TEG and the MEG, with adjustment to each group average age and sex proportions limbs and upper limbs/shoulders than TEG participants (p = 0.040, average effect: d = -0.61; p = 0.012, average effect: d = -0.58, respectively). On the other hand, for both lower limb (p = 0.000; large effect: d = -0.89) and upper limb strength (p = 0.000; large effect: d = -1.07) and aerobic endurance (p = 0.011), the MEG participants had worse results than the TEG participants, although with a small effect (d = -0.43) for aerobic endurance values. The MEG participants also had a more favorable general cognitive status (p = 0.001; mean effect: d = -0.74) than TEG participants, although they had more depressive symptoms (p = 0.015; mean effect: d = 0.51).

Discussion
The present study aimed to compare health indicators between older adults participating in multimodal exercise (creative dance combined with traditional Portuguese singing), those participating in traditional physical exercise, and those not actively engaged in physical exercise. To our knowledge, this is the first study showing the potential benefits of multimodal exercise based on creative dance practice in association with the interpretation of traditional Portuguese songs, especially when comparing the results of regular practitioners of this type of exercise with individuals not engaging in physical exercise. Regular practice of multimodal exercise, which combined creative dance with traditional Portuguese singing, seemed to result in better agility and balance, flexibility, and general cognitive status. The practice of traditional physical exercise has also been shown to be beneficial compared to the absence of regular physical exercise, revealing that participants in these types of programs have greater agility and balance, higher rates of strength and aerobic endurance. In the comparison of this multimodal exercise with traditional exercise, both showed benefits in different conditions, with multimodal exercise practitioners showing higher benefits in flexibility and general cognitive status. In contrast, traditional exercise practitioners showed greater benefits in strength, aerobic endurance, and depressive symptoms. However, both groups were below the diagnostic values of depression [33].
Traditional physical exercise programs, mostly multidimensional by design, aim to develop strength, balance, aerobic endurance, and flexibility [38]. This type of intervention has proven to be effective in promoting healthy aging, mainly due to the beneficial effects that it produces on balance, strength, and aerobic endurance. These improvements have direct consequences in carrying out daily tasks and reducing the risk of falls in older people [6,7,39,40]. In the present study, the participants in traditional physical exercise programs showed results consistent with previous interventions [6,7,39,40], although it was a cross-sectional study.
In older people, when intervention groups involving physical exercise are compared to control groups, even in randomized controlled studies, it is common to register significant differences between exercise groups and control groups, regardless of the type of exercise performed [6]. In addition to the benefits registered in the present study's TEG participants, the MEG participants, where creative dance was combined with traditional Portuguese singing, showed better rates of agility and balance, flexibility, and a more favorable general cognitive status compared to the NEG. Some interventions that include physical exercise practice based on dance were shown to be effective in improving balance and gait and were considered by the participants as a pleasant form of exercise [41]. However, Cruz-Ferreira et al. [19] found that physical exercise specifically focused on creative dance revealed beneficial effects of this practice on lower limb strength in 21% of practitioners, lower limb flexibility, agility, and dynamic balance in 13%, aerobic endurance in 10%, waist circumference in 8%, body mass index in 5% and satisfaction with life in 34%. In the same manner, other scientific research reported beneficial effects of dance practice not only on promoting balance, proprioception, strength, flexibility, posture, and aerobic endurance but also on improving social relationships and promoting positive emotions, thereby contributing to healthy aging [14][15][16][17][18]. Benefits from dance practice seem to come from the stimulation of brain functions related to memory, cardiovascular stimulation, cognitive stimulation, and the social and emotional interactions promoted in this type of activity [14].
Comparing usual practitioners of traditional exercise and practitioners of multimodal exercise in the present study revealed higher levels of flexibility and a better general cognitive status for the multimodal exercise practitioners, who engaged in creative dance with traditional Portuguese singing. In the meantime, the practitioners of traditional physical exercise revealed higher strength and aerobic endurance levels and fewer depressive symptoms. A relatively recent study with 30 sedentary women between the ages of 60 and 75 living in the community compared regular dance practitioners (3x/week, 60-min sessions) with walkers and stretching practitioners in an intervention that lasted eight weeks [42]. Dancing induced similar improvements to walking in cardiorespiratory fitness, lower limb muscle strength, and static balance. In addition, the practice of dance associated with traditional Portuguese singing seemed to induce brain function stimulation related to memory and influenced practitioners' general cognitive status, as suggested by other authors [14]. This is the first study to analyze a group of subjects in a multimodal exercise program that combines creative dance and traditional Portuguese singing to refer the practitioner to their previous experiences and cultural heritage while making body expression movements (adjusted and structured spontaneous exercises) compared to a group of people engaged in a traditional exercise program. The results of the present study support using this form of physical exercise as a strategy to promote healthy aging. Thus, it creates the foundation for performing random controlled interventions to prove the effects of multimodal exercise combining expressive dance and traditional Portuguese singing.
Findings from our study should be interpreted with caution due to its cross-sectional design. We consider this to be one of its main limitations since it does not establish cause-effect relationships. Even so, we believe that our findings can guide future randomized controlled trials and providing indications for the potential of multimodal exercise that combines expressive dance and traditional Portuguese singing. Therefore, longitudinal studies are needed to confirm the beneficial effects of our multimodal exercise approach. Another limitation is related to the fact that the intensity of the physical exercise sessions was not monitored and should be considered an essential requirement in future research.

Conclusion
This study found that the regular practice of creative dance, combined with traditional Portuguese singing, is associated with greater agility and balance, flexibility, and general cognitive status in older adults. The present study results suggest that this form of physical exercise seems to induce different and complementary benefits to the practice of traditional physical exercise for this population. These results provide evidence for creating physical exercise programs based on the association between creative dance and traditional Portuguese singing to promote healthy aging.


s2fieldsofstudy: Education Physics

text:
A modified peer instruction versus teacher’s instruction

Peer Instruction (PI) was introduced by Mazur [1] to help students learn physics concepts during lectures. Besides physics [2,3], PI has also been adopted in other STEM fields [4]. In this approach (Figure 1(a)), students answer a related question individually after a concept has been presented. Before they revote on the same question individually, they are asked to convince others their answer is correct during peer discussion. The percentage of correct answer typically increased after peer discussion [2,3]. However, Smith et al. [5] highlighted that the improvement may be due to copying, not because students actually learned how to reason correctly. To exclude copying, Smith et al. [5] modified Mazur’s PI protocol by adding a second question Q2 after the students revote on the first question Q1 (Figure 1(b)). Q2 is ‘isomorphic’ to Q1, meaning that it requires the application of the same concept but the ‘cover story’ is different [4]. The answer to Q1 is not revealed until after the individual vote on Q2. Here, we simplify Smith et al.’s PI protocol by removing the revote on Q1 (Figure 1(c)). Moreover, our Q1 and Q2 are similar, i.e. the same but some information given is different. Our PI protocol is thus the same as Mazur’s, except the pre and post discussion questions are not exactly the same. We compare the effectiveness of PI, in our protocol, to teacher’s instruction (TI) for a pair of similar questions, involving Lenz’s law, using Hake’s normalized gain [6] and a statistical test.

The study was conducted in our first-year Physics for Engineering course over two semesters. The pair of similar questions involving the application of Lenz's law is as follows The smaller circle in Figure 2 is the cross section of a solenoid. The cross section of the solenoid, and the conducting loop both lie on the page. The magnetic field outside the solenoid is zero. The magnetic field inside the solenoid is uniform, where the magnitude is given by 0 . The current i in the solenoid is _______ and ________.
(a) The magnetic flux through the conducting loop is increasing or decreasing? (b) The current induced in the conducting loop produces a magnetic field. The direction of this magnetic field in the area bounded by the conducting loop is into the page or out of the page? (c) The induced current in the conducting loop is clockwise (cw) or counterclockwise (ccw)?
The information given for the current in the solenoid is one of four possibilities: cw and decreasing, cw and increasing, ccw and decreasing, or ccw and increasing. For Q1, the current information is not the same for all students (to compel students to learn through peer discussion). Similarly for Q2. Each question is answered correctly only if all three parts are answered correctly; in this case 1 mark is awarded, otherwise, no mark is given. Before students attempted Q1, they were told that the probability of guessing all three parts correctly is only 0.125. After answering Q1, they were told Q2 will be similar to Q1, and they were asked to learn how to arrive at the answers to Q1 during peer discussion (Semester A, 39 students) and teacher's instruction (Semester B, 95 students). As an incentive to do so, Q2 (like Q1) carries a small credit. 5 minutes were allocated for answering each question. The duration for peer discussion was 10 minutes. For teacher's instruction (between 5 to 10 minutes), the instructor explained how to answer each part of Q1 generally as follows (a) How to calculate the magnetic flux through a loop in a uniform field. The flux is , which increases (decreases) if the current i increases (decreases). (b) Firstly, how to use the right-hand-rule to determine the direction of the solenoid field. Secondly, how to determine the direction of the induced field: if the magnetic flux increases (decreases), the induced field points in the opposite (same) direction to (as) the solenoid field. (c) How to use the right-hand-rule to determine the direction of induced current.
These points were already covered in the lectures in both Semester A and B, which is why we administered the pair of questions during the tutorial (problem-solving) session after the lectures. Near the end of the tutorial, the students worked on one practice question involving Lenz's law, which requires the derivation of the formula for the terminal velocity of a conducting bar sliding on a vertical conducting rail in a uniform horizontal magnetic field. The solution to this practice question was discussed by the tutor/instructor before the students attempted Q1.
The percentage of correct answer (PCA) for Q1 is 46% and 43% for PI and TI, respectively (Figure 3), which is in the lower end of the 35%-70% range proposed by Couch and Mazur [2] for the definition of a challenging but not too difficult question. The PCA for Q2 is 59% for both PI and TI, which is 13% and 16% higher than the PCA for Q1 for PI and TI, respectively. Hake's normalized gain, which is a rough measure of the effectiveness of a method of instruction, is defined as [6] where <> denotes class average, which in our case is the same as the PCA. The normalized gain (actual gain divided by maximum possible gain) is 0.24 and 0.28 for PI and TI, respectively. In other words, based on this measure, TI is marginally more effective than PI.
Furthermore, the PCA for Q1(a) (i.e. part (a) on magnetic flux) is high, 80% and 78% for PI and TI, respectively ( Figure 3). Moreover, the PCA for Q2(a) is very high, 95% and 93% for PI and TI, respectively. This shows that the low percentage (59%) of correct answer for Q2 (all three parts) for both PI and TI is mainly due to the inability of a high percentage of students in answering parts (b) and (c) correctly, which require a true understanding of Lenz's law and the right-hand rule that relates current and field directions. To assess whether there was a statistically significant difference between the Q1 (pre instruction) and Q2 (post instruction) marks, we used the paired samples Wilcoxon test [7] instead of the paired samples t-test since neither Q1 nor Q2 marks are normally distributed. The test shows that the median of the differences between the paired marks is statistically significantly different from zero (p value = 0.027) for TI, but not statistically significantly different from zero (p value = 0.306) for PI. In other words, TI elicited a statistically significant change in student's paired marks (from Q1 to Q2), but PI did not.
Hake's normalized gain and Wilcoxon's test show that TI is more effective than PI, in our protocol, for a challenging pair of similar questions involving Lenz's law. It would be interesting to replicate our pilot study to see if the same conclusion holds. Furthermore, our study could be extended to other pairs of similar questions. Pairs of isomorphic questions, and different difficulty levels could PI TI also be investigated. One could also study whether adding TI after PI in our protocol is more effective than either PI or TI alone.


s2fieldsofstudy: Education Medicine

text:
The Developmental Experiences of Expert Athletes in Sports with Different Levels of Risk

Background. Recent research has proposed that environmental factors a ﬀ ect the development of expertise. Objectives. The present study aimed to compare the history of training activities amongst expert athletes of different sport disciplines regarding the level of risk. Methods. Participants included 187 athletes from six sports disciplines, which were allocated to "low-risk," "medium risk," or "high-risk" groups based on the level of physical risk placed on the participating athletes. Participants completed a developmental history of athletes' questionnaire. Results. Findings revealed a significant difference amongst groups in terms of the mean age of beginning the sport (p < 0.001). Additionally, there was no significant difference between the total hours of deliberate practice and physical preparation amongst the three groups (p > 0.05). However, the low-risk group had an enormous amount of deliberate practice (p < 0.001) and physical preparation activities (p = 0.002) in the 6 to 12-year age band, and the medium-risk and high-risk sports groups had an enormous amount of deliberate practice (p = 0.01) and physical preparation activities (p = 0.003) in the age band of above 16 years. Conclusion. The present study provides useful information on the expertise acquisition process in different sport disciplines regarding the amount of practice relative to risk level.

INTRODUCTION
Genetic and environmental factors affect sports performance (1). Accordingly, studies conducted on expertise in sport have been divided into four developmental areas, including 1) biological (genetic) description of expertise development in sport (2,3), 2) training as the primary contributor to expertise development in sport (4,5), 3) the environmental factors influencing expertise development (6,7), and 4) the multifactorial approach which encompasses the previous three factors (8,9). One of the models having been taken into consideration in the area of the amount of practice is Côté's developmental model of sport participation (DMSP) (9,10). The DMSP deals with the importance of training patterns in the expertise acquisition process. The DMSP consists of three distinct stages, including sampling years (childhood, 6 to 12 years), specializing years (early adolescence, 13 to 15 years), and investing years (late adolescence, older than 16 years) (11). Based on this model, it is recommended that children in the sampling stage participate in different sport disciplines in the form of deliberate play (12). Deliberate play refers to playing games such as soccer in the backyard or basketball in the street with age-based modified rules. These forms of play are regulated and controlled by children or adults involved in the activity. These activities are inherently motivating and are designed to enhance enjoyment (12). Côté and Hay acknowledged that, in the specializing stage, it is better to limit children's participation in different sport activities, and the activities at this stage should be primarily in the form of deliberate play and deliberate practice (12). Deliberate practice is a kind of training done with the primary aim of improving performance. Coaches control this kind of practice, and the coaches' feedback and interventions are conducted to correct errors and improve motor performance (13). Criteria for deliberate practice are 1) Individualized design of effective practice, 2) Active responses to a task with an explicit goal, immediate feedback, and repetition, and 3) Individualized assessment of skill and design of future practice (4).
In the final stage (i.e., during the investment years), young athletes are usually only committed to one sport and are mainly engaged in deliberate practice. Accordingly, three testable principles have been proposed based on the developmental model of sport participation, including 1) as children, expert athletes participated in various sports rather than specializing in one sport. 2) expert athletes participated in deliberate play during childhood, rather than deliberate practice. 3) and lastly, expert athletes narrowed their participation in one or two sport disciplines during adolescence and adulthood, and mainly participated in deliberate practice, rather than deliberate play in adolescence and adulthood (9).
The philosophy of promoting widespread participation in sport and physical activity during the sampling stage is to create motivation for continued participation in sport-related activities. Kirk (14) states that different learning experiences during childhood not only develop physical capabilities but also increase a sense of competency in sport. During the specializing years, deliberate play allows children to experience different sports in different contexts and further grow sport-related excitement. Deliberate play makes it possible for children to be creative in performing their activities, without being worried about adults (i.e., their coaches and parents) intervening to show them the correct way to perform a skill (11). This is an implicit method for skill development, and these children are less likely to forget what they have learned (11). Finally, during the investing years, young athletes increase the amount of time allocated to deliberate practice and reduce the amount of time allocated to deliberate play as they transition from adolescence into adulthood (15).
Conducting a research study on professional ice hockey players, Soberlak and Côté (16) confirmed these three principles. The findings of their study showed that professional ice hockey players spent more than 10000 hours between the ages of 6 -20 years participating in sport-related activities, out of which they spent about 350 hours engaged in game-like activities in other sports. Moreover, 2200 hours were dedicated to the deliberate practice of sports other than hockey, 3000 hours were invested in organized hockey activities (i.e., deliberate practice), and more than 2400 hours were allocated to playing organized hockey games (17). Another study reported that about 50% of the total training time was spent participating in deliberate practice in one particular sport. In comparison, the same athletes spent 50% of their time participating in deliberate practice and deliberate play in other sports.
Nonetheless, it seems that the amount of hours spent on participation in deliberate practice depends on the type of sport (18). For example, triathletes usually reach their peak performance at an older age compared to ice hockey players. Thus they spent more time in their childhood participating in sports other than triathlons (11). Accordingly, it seems that it is difficult to declare the average hours of deliberate practice, deliberate play, and training in other sports each week during the period of sport participation, and it cannot be judged as a general rule. It may depend on the type of sport (9).
One of the systems used to classify sport is based on the degree of risk associated with participation in that particular sport. According to Zuckerman's definition (19) sports disciplines are classified into three categories: low-risk, medium-risk, and high-risk. Low-risk sports are associated with a lower chance of becoming injured. Sports such as jogging, bowling, golf, swimming, and aerobics fall into the low-risk category. Medium-risk sports are sports with the highest chance of becoming injured, but they are not generally associated with the risk of death. Sports such as boxing, karate, and American football are considered as examples of this category of sports. In contrast, high-risk sports are sports with the chance of severe injury or even death. High-risk sports include mountaineering, rock climbing, high-altitude parachute jumping, free fall, and in general, situations where individuals have to struggle with the problematic elements of nature.
Considering that the expertise acquisition process probably differs depending on the type of sport, the current study was conducted to compare the history of training activities among expert athletes of the low, medium, and high-risk sport disciplines. The pursuit of this aim is essential to obtaining information on the expertise acquisition process to help in the facilitation of officials in educational planning. It can help parents put their children in a better athletic development path.

MATERIALS AND METHODS
The experimental protocol was reviewed and approved by an Ethical Participant advising committee. We presented information about the study, purposes, procedures, etc., to all participants at the beginning of the study and obtained the informed consent of each participant.
Participants. The participants of this study consisted of 187 athletes competing at national and international levels (N = 70 females and N = 117 males), aged 25 -35 years, and from six sports disciplines with different degrees of risk (31 athletes in swimming and 28 in gymnastics as low-risk sport disciplines, 25 athletes in karate and 45 in wushu as medium-risk sport disciplines, and ten athletes in paragliding and 48 in rock climbing as high-risk sport disciplines), who were selected using availability and purposive sampling methods.
Apparatus, Task, and Procedure. Data were collected using the Hopwood developmental history of an athlete's questionnaire (DHAQ) (20). Participants' demographic information, the general pattern of their activities from their childhood until their current age, such as the age of beginning their primary sport discipline, the multiplicity of sports they had participated in, the amount of deliberate practice and play in their primary sport discipline, the amount of physical preparation (e.g., strength development, pilates, or any other exercises they had done to enhance the dimensions of their physical preparation), psychological preparation (e.g., relaxation and visualization exercises), and mental preparation (e.g., watching competitions of their primary sport discipline, reading books related to their primary sport, and watching educational videos) and the amount of deliberate play were assessed in this questionnaire. Then the gathered information was summarized and is reported in tables related to the three age bands of 6-12 years, 13-15 years, and 16 and above years.
Developmental History of Athlete's Questionnaire. Practice history (amount of practice and the type of practice activities) were collected using the Hopwood developmental history of an athlete's questionnaire (DHAQ) (20). The first section of the DHAQ is allocated to demographic data such as gender, date of birth, and level of competition.
The next section assessed the athletes' participation in deliberate play. Deliberate play refers to activities that are not very structured and encompass minimal adult supervision. Participation in deliberate play is optional, and the activities are enjoyable and done by inherent motivation and provide immediate satisfaction (12). Therefore, in this section, participants were asked to recall details of their participation in an enjoyable and informal sports game, which was similar to athletic competitions but embraced modified rules and equipment, and was done by minimal adult supervision. Similar to the approach adopted by Baker et al. (17); Berry, Abernethy, & Côté (21); Côté and Fraser- Thomas (9); participants identified all the activities in which they were involved fitting this explanation along with the age at which they regularly participated in each activity. The number of months per year and the average number of hours per week during which they were involved in that activity were recorded for each year of their participation in an informal and entertaining sports game.
The next section of the DHAQ is assessed engagement in the athletes' primary sport. First, the athletes were asked to provide general information about their participation in their primary sport. For each year of their participation, athletes recalled the total number of months in which they were involved in their primary sport, the average number of hours per week during which they participated in the training, and the average number of training sessions per week.
The next section assessed is engagement in physical preparation (e.g., strength and conditioning, weights, fitness, pilates, yoga, flexibility). The number of months per year and the average number of hours per week during which they were involved in that activity were recorded for each year of their participation.
The final section of the DHAQ assessed engagement in mental preparation (e.g., working with a psychologist, video analysis/ review). The number of months per year and the average number of hours per week during which they were involved in that activity were recorded for each year of their participation.
The validity and reliability of this instrument have been assessed using different methods. For instance, the accuracy between data about the age of beginning the sport and years of participation obtained from various sources such as athletes, their parents, and the resume of each athlete has been reported to be 79% to 100% (20). For several sports activities, the agreement between the primary and secondary sources of data has also been strong, with the values of agreement ranging between 88% and 100% (20). Although the validity and reliability of recalling the amount of time for participation in deliberate play have only been assessed in a small number of studies, it does not seem to be as strong as the validity and reliability of recalling the amount of time for participation in deliberate practice. For instance, Memmert, Baker, and Bertsch (22) obtained a Pearson correlation coefficient of 0.79 for total hours of participation in deliberate practice. However, the Pearson correlation coefficient for total hours of participation in deliberate play was 0.70 in comparison.
Similarly, Soberlak and Côté (16) indicated the correlation between hours of participation in deliberate play reported by athletes and parents with a 30% difference compared to a 1% difference for hours of participation in deliberate practice. Despite lower levels of validity and reliability for recalling hours of participation in deliberate play than those in deliberate practice, the reported values still show an acceptable amount of agreement. In other words, it seems that the athlete's participation in sport-related activities is recalled with acceptable validity and reliability (23). In total, the validity and reliability of recalling the hours of participation in training activities have been quite good and have fluctuated with Pearson correlation coefficients ranging from 0.59 to 0.97 (20).
Data Analysis. Data were analyzed using a one-way analysis of variance (ANOVA) and the phi-coefficient analysis tests via the statistical package for the social sciences (SPSS) software version 23.

RESULTS
17.7% of the study sample was made up of athletes in swimming, 15.4% of athletes in gymnastics, 9.7% of athletes in karate, 25.7% of athletes in wushu, 4% of athletes in paragliding, and 27.4% of athletes in rock climbing. Athletes in swimming and gymnastics, karate and wushu, and paragliding and rock climbing were classified into low-risk, medium-risk, and high-risk sport disciplines. Table 1 compares the age of beginning the sport among low, medium, and high-risk sport disciplines. Values in Table 1 revealed that the mean age of beginning the sport is lowest in low-risk sport disciplines, and most significant in high-risk sport disciplines. Based on the results of the one-way ANOVA, there was a significant difference between sport disciplines in terms of the mean age of beginning the sport (p < 0.001). Post hoc analyses using the Scheffe post hoc criterion for significance indicate that the mean start age of participants in low-risk sport disciplines was significantly lower than that in both medium-risk and high-risk sport disciplines (p < 0.001). Also, this means it was significantly lower in medium risk sport disciplines in comparison to that of high-risk sport disciplines (p < 0.001). Table 2 shows the frequency of athletes in low, medium, and high-risk sport disciplines regarding the multiplicity of sports they had participated in.
The values in Table 2 reveal that 61.7% of the athletes in low-risk sports were single-sport athletes, while 37.3% participated in multiple sports. Also, 52.9% of the athletes in medium-risk sports reported being single-sport athletes, and 47.1% were multi-sport athletes. Additionally, 37.9% of those in high-risk sports were singlesport athletes, whereas 62.1% were multi-sport athletes. Results of the chi-square test revealed that there was a significant relationship between sport discipline and the multiplicity of sports (p < 0.026). Hence, it can be said that a higher percentage of athletes in low-risk and mediumrisk sport disciplines were single-sport athletes, and a more significant percentage of athletes in high-risk disciplines participated in multiple sports. Table 3 shows the mean hours of practice among low, medium, and high-risk sport disciplines in the 6 to 12 year age band.
Results of the one-way ANOVA (see Table 3) revealed that the hours of deliberate practice (p < 0.001) and physical preparation activities (p = 0.002) in the age range of 6-12 years were significantly different between the three groups.
Post hoc analyses using the Scheffe post hoc criterion for significance indicated that the mean hours of deliberate practice of participants in lowrisk sport disciplines was significantly lower than that in both medium-risk (p = 0.020) and high-risk (p < 0.001) sports disciplines, but there was no significant difference between medium-risk and high-risk sport disciplines (p = 0.196). Also, the mean physical preparation activities of participants on low-risk sport disciplines were significantly lower than those in high-risk sport disciplines (p = 0.002). However, there was no significant difference between medium-risk and high-risk sport disciplines (p = 0.161), and between low-risk and medium-risk sport disciplines (p = 0.211).  Table 4 shows the mean hours of practice among low, medium, and high-risk sport disciplines in the 13 to 15 year age band. The oneway ANOVA revealed no significant difference in the mean hours of practice among low, medium, and high-risk sport disciplines (p > 0.05). Table 5 shows the mean hours of practice among low, medium, and high-risk sport disciplines in the age band above 16.
Results of one way ANOVA in Table 5 revealed that the hours of deliberate play (p = 0.015), deliberate practice (p = 0.010), physical preparation (p = 0.003) and mental preparation (p = 0.019), activities in the age band above 16 years were significant difference between the three groups.
Post hoc analyses using the Scheffe post hoc criterion for significance indicate that the mean hours of deliberate play of participants in lowrisk sport disciplines were significantly lower than that in high-risk sport disciplines (p = 0.028). However, there was no significant difference between medium-risk and high-risk sport disciplines (p = 0.907), and between lowrisk and medium-risk sport disciplines (p = 0.060). The mean hours of deliberate practice of participants in low-risk sport disciplines were significantly lower than that in both medium-risk (p = 0.012) and high-risk (p = 0.015) sports disciplines, but there was no significant difference between medium-risk and high-risk sport disciplines (p = 0.759).
The mean hours of physical preparation of participants in low-risk sport disciplines were significantly lower than those in medium-risk sport disciplines (p = 0.003). However, there was no significant difference between medium-risk and high-risk sport disciplines (p = 0.254) and between low and high-risk sport disciplines (p = 0.233).
The mean hours of mental preparation of participants in low-risk sport disciplines were significantly lower than that in medium-risk sport disciplines (p = 0.019). However, there was no significant difference between medium-risk and high-risk sport disciplines (p = 0.378) and between low and high-risk sport disciplines (p = 0.378).

DISCUSSION
It seems that the amount and type of practice vary depending on the type of sport being practiced (13). Accordingly, the present study aimed to investigate the history of training activities among expert athletes of different sport disciplines with varying degrees of risk.
The findings of the present study showed that the age of beginning the sport varied depending on the level of risk associated with the sport. The age of initial participation in the sport was significantly lower in low-risk sport disciplines than medium and high-risk sport disciplines. The age of beginning the sport was lower in mediumrisk sport disciplines than in high-risk sport disciplines. It seems that the age of beginning the sport in its specialized form depends on several factors such as the physical, physiological, cognitive, psychological needs of that sport in addition to the motor abilities possessed by the athlete.
Motor abilities are contextual and fundamental components of motor skills. Through carrying out many perceptual-motor tests, Fleishman (24) identified several distinct human motor abilities. According to him, there are eleven perceptualmotor abilities, including multi limb coordination, control precision, response orientation, reaction time, speed of arm movement, rate control, manual dexterity, finger dexterity, arm-hand steadiness, wrist/finger speed, and aiming. Furthermore, Fleishman identified nine physical proficiency abilities, including static strength, dynamic strength, explosive strength, trunk strength, flexibility, dynamic flexibility, gross body coordination, and fit stamina (to which most people refer as physical preparation). According to various studies, perceptual-motor abilities and physical proficiency required across sports are different (25).
Additionally, sports skills differ in terms of their underlying motor abilities; they also differ in terms of the cognitive demands of the sport participant. Some sport skills consume few cognitive resources while other sport skills depend on the speed of one's mental ability to solve a problem and process relevant information to successfully perform the task. Also, the biochemical demand, and cardiorespiratory and neuromuscular coordination of different sports vary widely, and the psychological needs of sports are also different (26). As a result, athletes that participate in stress-inducing sports must be trained to properly deal with such psychological variables (26).
Overall, it seems that differences between the ages of beginning low, medium, and high-risk sports depend on the different physical, physiological, motor, cognitive, and psychological demands required by that sport. The age of beginning the sport may also depend on the age of reaching peak athletic performance. Some studies have estimated the age of peak athletic performance to be different across sports (27). The present study estimated the age of reaching peak athletic performance to be 16.87 years in low-risk sport disciplines (e.g., gymnastics and swimming), 21.78 years in medium-risk sport disciplines (e.g., karate and wushu), and 22.32 years in high-risk disciplines (e.g., rock climbing and paragliding). According to Ericsson et al. (13), one must have at least ten years of experience in practicing a specialized discipline to reach peak athletic performance. As a result, the difference between the ages of beginning low, medium, and high-risk sport disciplines in our study explains why athletes in the respective sports reach peak performance at different ages.
Another finding revealed in the present study showed differences among low, medium, and high-risk disciplines in terms of the multiplicity of sport participation. In low and medium-risk disciplines, training hours of other sports were less, and the athletes were more likely to participate in only one sport. However, high-risk sports athletes were much more likely to participate in multiple sports. Perhaps one possible explanation for this observation is that the age of beginning the sport was greatest in high-risk sport disciplines. Thus athletes had more time to participate in additional sports and comprehensive training as a child.
In addition to the age of beginning the sport and age of reaching peak athletic performance, the amount of deliberate practice and physical preparation activities in the three types of sports were different in the three stages of sampling, specializing, and investing in terms of the degree of risk. In the age range of 6-12 years, the hours of deliberate practice and physical preparation activities were more in low-risk sport disciplines than in other sport disciplines. Whereas, at the investing stage (over 16 years of age), the hours of deliberate practice and physical preparation activities were more in medium and high-risk sport disciplines than in low-risk sport disciplines. Also, the difference in the amount of practice can be attributed to the differences between the physical, physiological, cognitive, and psychological needs of the different sports. For instance, Soberlak and Côté (16) showed in a study that professional ice hockey players spent more than 10,000 hours from the ages of 6 to 20 years participating in sports. Within the allotment of 10,000 hours, about 2,300 hours were spent participating in sports other than hockey, 350 hours were dedicated to playing deliberate games other than hockey, more than 3,000 hours were dedicated to deliberate hockey practice 2,400 hours were involved in deliberate hockey games. The significant finding in the study conducted by Soberlak and Côté (16) was that a large amount of deliberate play and participation in other sports had occurred before the age of 15. A large amount of deliberate practice had occurred after the age of 15. This contrasts with a study conducted by Tan and Low (28) investigating practice allocations within swimmers. They concluded that a large part of the elite swimmers' activities was allocated to deliberate practice (i.e., more than 5000 hours), while other activities such as deliberate play were less than 1000 hours. This shows that elite swimmers have not allocated much time to voluntary and inherently motivated swimming activities.
Another finding of the present study showed no statistically significant difference between the athletes of the low, medium, and high-risk sport disciplines in terms of the average hours of training mental skills in the 6 to 12 years and 13 to 15 years. One of the possible reasons was the small amount of training mental skills services offered to the athletes. Our findings revealed that the amount of mental skills training in the six sports disciplines was 25. Three hours on average in the 6 to 12-year age range, 97.73 hours in the 13 to 15-year age range, and 142.53 hours in the over 16-year age range. By reviewing 45 different studies, Weinberg and Williams (29) concluded that 38 cases (i.e., 85% of the cases being reviewed) were indicative of the positive effect of training mental skills on performance. Despite the benefits of mental training skills, Weinberg and Williams (29) reported that many athletes and coaches do not actively engage in mental skills training practices. The authors indicated that this is unawareness of how to teach and train mental skills, the belief that psychological characteristics are inherent and unchangeable, and the lack of time to devote to mental skills training. Hence, it seems that we must build a culture regarding this case to highlight the importance of dealing with mental skills for athletes, coaches, and authorities.

CONCLUSION
In conclusion, the findings of this study show that the age of beginning a sport, the multiplicity of sport participation, as well as the amount of deliberate practice and deliberate play within the three age ranges of 6 to 12, 13 to 15, and above 15 years of age, depends on the type of sports being practiced.
We observed that athletes involved in all of these sports trained mental skills to a much lesser extent than training for the sport. Given the importance of mental skills in the progress of athletic performance, it is suggested that more attention is paid to training and practicing mental skills in addition to practicing the sports discipline.

APPLICABLE REMARKS
• Coaches explain to athletes the importance of psychological skills in achieving high levels of performance. • Coaches should note that the age of beginning a sport depends on the type of sports being practiced.