Sports, dietary habits, self-perception and BMI in a sample of young Italian athletes

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1 Sport Sci Health (2011) 6:67 76 DOI: /s ORIGINAL ARTICLE Sports, dietary habits, self-perception and BMI in a sample of young Italian athletes Camilla Cerizza Elena Campanini Giacomo Di Benedetto Cinzia Menchise Received: 24 November 2010 / Accepted: 13 January 2011 Springer-Verlag 2011 Abstract The concept of health closely associates absence of disease and a state of overall well-being (physical, mental, social). This state can be achieved by a healthy dietary habit and by engagement in physical activity. Despite this, children and teenagers are increasingly becoming overweight and obese. The aim of this study was to evaluate whether different exercise habits, dietary habits and self perceptions could influence anthropometric characteristics, in particular the body mass index (BMI), in selected participants in high-level sport aged between 10 and 18 years. An anonymous questionnaire consisting of 20 multiple choice questions was submitted to 1,096 participants (757 males and 339 females) undergoing the preparticipation physical examination that is necessary before taking part in high-level sport in Italy. A descriptive analysis was developed based on relative frequencies because of the qualitative nature of most of the questions with the aim of determining the influence on BMI of: each type of sport; training hours; training hours and type of sport; individual diet (carbohydrates and proteins); habit with regard to breakfast; hours of training and diet; and psychological motivations for eating. Only the combined action of a proper dietary habit and an adequate number of training hours resulted in a radical shift towards an optimal BMI. Moreover, a "negative" psychological motivation for eating possibly influenced the distribution of BMI even in athletes who followed a correct dietary and training lifestyle. Key words Overweight Obesity Training hours Lifestyle behaviour Introduction C. Cerizza C. Menchise CMS Sport Medical Center, Sesto San Giovanni, Milan, Italy E. Campanini Università Vita-Salute San Raffaele, Department of Clinical Psychology and Psycotherapy Milan, Italy G. Di Benedetto ( ) Politecnico di Torino, Department of Mechanics, Via Duca Degli Abruzzi 24, Turin, Italy giacomo.dibenedetto@polito.it Over time, the concept of health has often changed. Nowadays the condition of absence of disease is closely associated with a state of overall well-being (physical, mental, social) [1]. Different factors are involved in achieving this state, the first of which are healthy dietary habits and physical activity. On the contrary, a sedentary lifestyle may lead to the onset of major diseases, including becoming overweight or obese, metabolic syndrome, cardiovascular diseases, osteoporosis and postural deformities [2]. The World Health Organization estimates that the level of physical activity is decreasing among young people all over the world. It is also known that fewer than one-third of young people are sufficiently active [3]. During the last decades, the lifestyle of Italian young people has also changed: spontaneous physical activity has been replaced with sedentary habits [4].

2 68 A study carried out on a large sample of Italian people by ISTAT (National Institute of Statistics) showed that only 20% of interviewees were physically active [5]. However, despite an increase in the number of school-age children belonging to sports clubs (mainly soccer) [6], children and teenagers are increasingly becoming overweight or obese. In a previous study [7], a sample of 238 male soccer players at the first preparticipation physical examination (mean age 11.9 years) revealed that 31.1% were overweight and 13.5% were obese. These results are in agreement with data obtained in other national and international studies [8 12]. Therefore the effects of increased physical activity (more structured and less spontaneous) have been negated by increasingly sedentary lifestyles that include higher food intake than required to meet real needs [13]. To inform the Italian population the INRAN (National Research Institute for Food and Nutrition) has issued guidelines for healthy eating [14]. Unfortunately, they have had no effect on Italian dietary habits [13]. In Italy since 1982 (Ministerial Order 02/18/1982) people who want to participate in sport at a competitive/performance level have to yearly undergo a preparticipation physical examination. To identify those who are at risk of developing disease, anthropometric characteristics, and the cardiovascular and respiratory systems are evaluated. High-level sport (in accordance with the definition of sport agonistico in Italian legislation) covers any form of sporting activity (organized by the National Sports Federations and by the sport promotion organizations recognized by CONI and the Ministry of Education) practised systematically and/or continuously to achieve a certain performance level (circular number 7 01/31/1983 based on Ministerial Order 02/18/1982). The aim of this study was to determine whether different exercise habits, dietary habits and different self-perceptions could influence the anthropometric characteristics, in particular the body mass index (BMI), in selected participants in high-level sport in the age range 10 to 18 years. Materials and methods Data were collected during the period between 1 January and 31 December 2009 at the CMS Sport Medical Center of Sesto San Giovanni (Milan, Italy) using an anonymous questionnaire submitted to 2,148 participants (1,584 males and 564 females). They were randomly selected from among around 4,000 participants who were undergoing physical examination for participation in high-level sport. All participants were informed about the characteristics and purposes of the study by a note posted at the entrance to the Center. We present here the results from 1,096 participants (757 males and 339 females) in the age range 10 to 18 years. Anthropometric data of the analysed sample are shown in Table 1. The study included a first part reporting the clinical history (age, sex, weight, height, blood pressure) collected during the medical examination and a second part which consisted of the questionnaire with 20 multiple choice questions divided into three sections: the participants sport and dietary habits, and psychological and behavioural aspects of food intake, as discussed in the following sections. Sports Sport Sci Health (2011) 6:67 76 Table 1 Anthropometric data (means±standard deviation) Age No. Weight Height BMI (years) of subjects (kg) (cm) (kg/m 2 ) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±3.04 The participants had to indicate the sports in which they participated (athletics, soccer, rugby, cycling, cross-country skiing, swimming, volleyball, basketball, water polo, and other sports), the number of training days per week and the number of hours per day. Multiplying the values for the last two questions gave the number of training hours per week which represented the workload done in a week. Some responses were grouped into classes in order to obtain for each class a more representative sample. The comparison with BMI was done using these classes. Some sports categories were grouped together. Cycling and cross-country skiing were grouped together because both sports have a high cardiovascular and ventilatory demand [15] and the athletes have the highest values of oxygen consumption (>80 ml/min/kg in a male cross-country skier, and 75 ml/min/kg in a male cyclist) [16]. The athletes can sustain their level of activity for a long time using oxidative energy sources and these sports especially are approached in an individual way. Soccer and rugby are both team sports, with a medium high cardiovascular demand [15] characterized by changes in pace, during which energy is supplied by both aerobic and anaerobic metabolism. The training hours per week were divided into three classes: less than 4 hours per week (light training), between 4 and 8 hours (medium training), and 8 hours or more (intense training).

3 Sport Sci Health (2011) 6: Dietary habits of the participants In this section the participants had to answer questions related to eating habits and diets followed. The questions asked the respondents to indicate how many times per week they consumed carbohydrates, bread and pasta separately, protein (meat and fish), and fruit and vegetables. The questions with multiple answers allowed the following responses: twice per day, once per day, four or five times per week, two or three times per week, rarely, never. For further analysis, the responses were grouped into three macro groups (carbohydrates, proteins and vegetables) and for each group four different levels of frequency of consumption were identified: 1. Very high, with at least one serving per day of either bread or pasta for carbohydrates, both meat and fish for proteins, and both fruit and vegetables for the macro group of vegetables. 2. High, with at least one serving per day. 3. Average, with about three servings per week. 4. Low, less than one serving per week. Given the low consumption of fruit and vegetables, any kind of vegetarian diet was analysed. Dietary habits were also analysed by dividing the participants into two macro groups: those with a correct nutritional habit (those with a high carbohydrate diet, i.e. at least two servings per day, with one portion per day of meat or fish), and those with an incorrect nutritional habit (those with a low intake of carbohydrates, i.e. one serving per day, and a low or high protein intake). In this way we tried to reproduce the definition of a balanced diet (65% carbohydrate and 35% protein) and unbalanced diet (high protein intake or otherwise inadequate) [14]. Questions regarding habit with regard to breakfast were also included in the questionnaire. Responses were classified separating people who did not have breakfast from those who did (regardless of the food eaten). Psychological and behavioural aspects of food intake This section included three questions. Participants had to recognize themselves in relation to their own weight (underweight, normal weight, overweight, obese). In the second question ( I often eat because ) they had to give the motivations underlying their eating habits, and in the last question ( I eat more when ) they had to indicate the social situations that could increase their food intake (i.e. I am in good company, I feel alone, watching TV, everybody is eating, I am influenced by advertising, I am in an unfriendly environment ). The answers regarding the second question ( I often eat because ) were classified into two classes, one regarded as a control ( I eat because I'm hungry ) and all the others ( I feel low, I feel unhappy, I feel alone, I'm anxious, I want a reward, I'm sad, I'm depressed, I'm worried, I'm bored ) as "negative" motivations because they deviate from a normally expected response. This question would determine if food could be used as a reward for negative feelings, and if the participants were aware that these feelings were linked to changes in BMI. The responses to the second question were also analysed in relation to training hours to determine if the reasons for the "negative" answers could affect some of the benefits of training. Statistical analysis A descriptive analysis was developed based on relative frequencies because of the qualitative nature of most of the questions. Based on the percentile values for BMI for the population of Northern Italy presented by Cacciari et al. [17], the participants were classified according to two procedures. In the first procedure, based on four classes (4C classification), the participants were classified using the 25th, 50th and 75th percentiles: those with a BMI less than the 25th percentile comprised the first class, those with a BMI between the 25th and 50th percentile the second class, those with a BMI between the 50th and 75th percentile the third class, and those with a BMI above the 75th percentile the fourth class (Table 2). In the second procedure, based on seven classes (7C classification in Table 2), the participants were classified using the 10th, 25th, 75th, 90th and the 97th percentiles. The classes were applied in relation to the different variables (sports, dietary and psychological habits) giving the relative weight of each class (expressed as a percentage derived from number of participants belonging to the class divided by the total number of participants). These classes were compared with the reference population (the percentages derived directly from percentiles) [17]. The two classifications had different objectives. The 7C classification described the changes in BMI with respect to reference data with particular attention to the extremities of the percentile distributions (underweight, overweight and obese), while those between the 25th and Table 2 4C (gross) and 7C (fine) classifications Class 4C 7C 1 BMI 25th percentile BMI 3rd percentile 2 BMI 25th 50th percentile BMI 3rd 10th percentile 3 BMI 50th 75th percentile BMI 10th 25th percentile 4 BMI >75th percentile BMI 25th 75th percentile 5 BMI 75th 90th percentile 6 BMI 90th 97th percentile 7 BMI 97th percentile

4 70 75th percentile were grouped together. On the contrary, in the 4C classification the classes were equally sized (each covered one-quarter of the reference population) with the aim of describing how the athlete population differed from the reference population throughout the range of permissible values, exploring even the differences between participants belonging to the 25th and 50th percentile and those belonging to the 50th and 75th percentile. The use of the two classifications allowed the data for the entire population to be normalized, regardless of age, because despite the differences in absolute values of BMI associated with different ages, inclusion in certain percentile groups allowed the age data to be considered homogeneous. Class values obtained on the population analysed were compared with those of the reference class of Cacciari et al. [17] in order to determine whether different exercise habits, food and other motivational attitudes toward food, influence the distribution of BMI. In detail: The influence of each type of sport The influence of training hours The influence of dietary habits (carbohydrates and proteins) The influence of habit with regard to breakfast The combined influence of training hours and diet The influence of motivations for eating different from the control ( I eat because I m hungry ) Results Type of sport Sport Sci Health (2011) 6:67 76 Figure 1 shows the results of the 4C classification for the different sport groups. The 4C classification results were compared with the reference values derived by Cacciari et al. [17]. In general those who participated in every sport (except basketball, volleyball and water polo) showed a better distribution (i.e. small values in the extremities of the BMI percentile distributions) with respect to the reference values. The highest percentage of subjects with a BMI under the 50th percentile was seen among those who engaged in athletics (about 72%), and fewer than 15% of this group were in class 4 (between the 75th and 100th percentile). In the 7C classification (figure not shown), none of those engaged in cycling or cross-country skiing, and only 4% of those engaged in athletics, had a BMI above the 90th percentile. Fig. 1 The 4C classification for each sport group (black bars analysed population, grey bars reference population)

5 Sport Sci Health (2011) 6: Fig. 2 The 4C classification (top row) and 7C classification (bottom row) for the three categories of training hours per week (dark grey bars low training load, light grey bars medium training load, black bars intense training load, white bars reference) Training hours Figure 2 shows the results of the 4C and 7C classifications in relation to training hours per week. A reduction in BMI among young people engaged in sport was evident, especially when the sport was practised intensively. A clear and stable reduction in BMI was seen with training of only 8 hours or more per week. With intense training, subjects moved to the central classes, with more than 55% below the 50th percentile and 35% between the 25th and 50th percentile in the 4C classification, and a reduction in the percentages in the extremities (below the 10th percentile and above the 90th percentile) in the 7C classification. This trend was more evident if only individual sports (tennis, athletics, swimming, cycling and cross-country skiing) were considered, for which the same effect was seen with training of only medium intensity (data not shown). Dietary habits It was clear that a diet high in carbohydrates (very high and high intake) resulted in lower BMI values, with 60% of subjects in classes 1 and 2 of the 4C classification. Conversely, the BMI of those with a lower carbohydrate intake (average and low) was higher (60% in classes 3 and 4). A similar trend was seen in relation to protein in the diet. Among those with a diet very high in protein and those with a diet low in protein, 60% and 47%, respectively, had a BMI below the 50th percentile, and 16% and 23%, respectively, were in the extremities (below the 10th and above the 90th percentile). Figure 3 shows the results of the comparison of correct and incorrect eating habits. It is apparent that good nutritional habits resulted in a better distribution of BMI, with more than 30% in class 2 of the 4C classification and around 46% in classes 3 and 4. Conversely incorrect dietary habits led to more than 53% in the last two classes. Breakfast habits The results obtained comparing those that had or did not have breakfast were very interesting, and are shown in Fig. 4. A high percentage of those in the habit of having breakfast were in classes 1 and 2 of the 4C classification (around 54%) compared to 45% of those who did not have breakfast. These results confirm those reported by Vanelli et al. [18] relating to children between 6 and 14 years of age.

6 72 Sport Sci Health (2011) 6:67 76 Fig. 3 Correct and incorrect dietary habits (black bars correct dietary habits, white bars incorrect dietary habits, grey bars reference values; top 4C classification, bottom 7C classification) Fig. 4 The 4C classification for breakfast habits (black bars those who have breakfast, white bars those who do not have breakfast, grey bars reference values)

7 Sport Sci Health (2011) 6: Fig. 5 The 4C and 7C classifications for dietary habits in relation to hours of training per week (black bars correct dietary habits, white bars incorrect dietary habits, grey bars reference values) Combining dietary habits and training hours Figure 5 shows the BMI in relation to training hours and eating behaviour (correct and incorrect). It was evident that only the combined action of a proper diet and an adequate number of hours of training would result in a radical shift towards optimal values of BMI. In fact, with fewer than 4 hours of training (Fig. 5, left), no clear difference between the two dietary habits was seen. However, with more than 8 hours of training per week (Fig. 5, right) and a proper diet, the BMI tended to concentrate in the central classes, with more than 40% in class 2 of the 4C classification, while in the 7C classification the extremes completely disappeared. Motivations for eating other than the control ( I eat because I'm hungry ) Those who provided a negative response (responses such as I feel low, I feel unhappy, I feel alone, I'm anxious, I want a reward, I'm sad, I'm depressed, I'm worried, I'm bored ) had a higher BMI than those who provided the control response, with more than 33% in class 4 of the 4C classification (Fig. 6, left panel). This trend persisted even with more than 8 hours training per week (Fig. 6, right panel), while the distribution of participants that gave the control answer changed dramatically. It is interesting that this question produced a high percentage of abstentions (about 30%) and that 80% of participants provided the control response ( I eat because I'm hungry ). Self-perception (results not shown) seemed consistent with the BMI results, indicating that people who participate in competitive sport have a coherent self-image. Discussion Subjects who participated in athletics showed the best BMI distribution, followed by those who participated in cycling and cross-country skiing. The BMI distribution tended to improve with increasing training hours. An analysis of the entire sample indicated a cut-off of 8 hours of training per week for a net improvement in BMI. This is consistent with other national and international literature. An Italian study [19] of 2,845 middle school students in Tuscany (1,466 boys and 1,379 girls) aged between 11 and 13 years showed that to avoid the risk of developing

8 74 Sport Sci Health (2011) 6:67 76 Fig. 6 Sometimes I eat because I am hungry (Control response). All the other responses are classified as negative. On the left panel all the answering participants were shown, while on the right one just the responses of subject with an intensive training per week. In black were reported the histograms referred to control response, in white the negative response ones, while in dark grey the reference values obesity in adulthood at least 5 hours of sport per week are needed. Andresen et al. [20] found that for optimal development a child needs at least 90 minutes per day of motor activity. The American guidelines for physical activity indicate that children and adolescents should get at least 60 minutes daily of physical activity divided between moderate aerobic exercise (running) and intense strengthening activities (gymnastics or push ups) in order to effectively prevent becoming overweight and the development of obesity, diabetes and cardiovascular disease [21]. A correct daily diet (65% carbohydrate and 35% protein [14]) dramatically moves the BMI distribution towards optimal values. This result is confirmed by the finding that those who have breakfast and also follow the directions for proper eating have a lower BMI than those who do not [18]. A relationship between self-image and the BMI data emphasizes that those who engage in high-level sport have an appropriate perception of the body and implement consistent behaviour to meet their needs. Psychological motivations for eating may greatly influence the distribution of BMI even in athletes who follow a correct dietary and sport training lifestyle. In conclusion, a proper lifestyle, in terms of dietary and physical habits and in terms of psychological motivations for eating, avoids young people becoming overweight and obese. The promotion of physical activity among young people should become a priority in every country, especially nowadays when spontaneous physical activity is often replaced by sedentary behaviour (i.e. computer games, internet, television). Acknowledgements The authors wish to thank their colleagues of CMS Sesto San Giovanni, Mrs C. Arrigoni, Mrs B. Cassinadri and Mrs R. Di Lieto for their invaluable collaboration in questionnaire submission, Mr M. Fiorillo for the first statistical elaboration of the data collected, and Dr Ilaria Donelli for her suggestions on English translation. The authors have no professional relationships to disclose with companies or manufacturers who will benefit from the results of the present study. Conflict of interest statement The authors declare that they have no conflict of interest relating to the publication of this manuscript. References 1. Italian Ministry of Education Bamman K, Peplies J, Pigeot I et al (2007) IDEFICS: a multicenter European project on diet and lifestyle related disorders in children. Med Klin (Munich) 102(3):

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