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e objective of this study was to assess the relationship of dietary intake of energy, iron, calcium and nutritional status of athletes with injuries and performance during training. A cross sectional study design was carried out on 282 athletes (192 males and 90 females) between 18 – 30 years participating in middle and long distance races. Socio demographic, socio-economic characteristics, injuries and performance were assessed using a pretested semi structured questionnaire. Dietary intake of energy, calcium and iron was determined using an individual 24- hour dietary recall method and nutrient intakes computed using Nutri- survey and food composition table. Nutritional status of athletes was measured as BMI. Results showed that major hindrances to expected performance were injury, loss of breath and fatigue. More females were at risk of suffering injuries than males. Dietary intake of energy, calcium and iron were low especially among females. Only 6.3% of females met the RDA for energy, 12.3% met the RDA for calcium and none of them met the RDA for iron. The nutritional status of athletes in terms of BMI was poor with significantly more females being underweight compared to male athletes. The study concluded that adequate dietary intake of energy, calcium and iron, and nutritional status of athletes are necessary to ensure performance and obviate injury. Keywords: BMI, Dietary Intake, Injuries, Nutritional status, RDA --------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 05-07-2017 Date of acceptance: 24-07-2017 --------------------------------------------------------------------------------------------------------------------------------------- I. Introduction Kenyan athletes are renowned over the World for their endurance running in middle and long distance races. They have won approximately 40% of the major international races in this

status of athletes are necessary to ensure performance and obviate injury. Keywords: BMI, Dietary Intake, Injuries, Nutritional status, RDA --------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 05-07-2017 Date of acceptance: 24-07-2017 --------------------------------------------------------------------------------------------------------------------------------------- I. Introduction Kenyan athletes are renowned over the World for their endurance running in middle and long distance races. They have won approximately 40% of the major international races in this category that they have participated in during the last decade Fudge, (2006) [1] . However, the athletes have on occasion suffered injuries during/ prior to competition and these have impacted their performance. These injuries are presumed to be nutrition related, and inadequate dietary intake of energy and micronutrients especially iron and calcium. Over the years nutrition has been identified as the key component to athletes training regimen. It is believed that aside from the limits imposed by heredity and training, no single factor plays a greater role in optimizing performance than diet Tipton, (2010) [2] . The International Association of Athletic Federations (IAAF) [3] Consensus Statement on Nutrition for athletics (2007) states that well chosen foods will help athletes train hard, reduce risk of illness and injury, and achieve performance goals, regardless of the diversity of events, environments, nationality and level of competitors. Training for sports in Kenya is not organized by the Government and is left to individual or group initiative. Little of is therefore known about dietary practices, nutritional status and training habits of Kenyan athletes, and indication are that there is no structured nutrition training program for the athletes. The dietary intake of many athletes follows population diet rather than public health or sports nutrition recommendations. The diets of Kenyan athletes consist mainly of their basic staple diets based predominantly on energy rich foods including cereals and root crops. These have been presumed to provide adequate nutrition for efficient and effective performance if adequately consumed. Female athletes are more vulnerable to poor nutrition resulting to poor performance and injuries because they strive to achieve a lower body weight or low body fat content which may increase the risk of amenorrhea, negative energy balance and eating disorders consequently

root crops. These have been presumed to provide adequate nutrition for efficient and effective performance if adequately consumed. Female athletes are more vulnerable to poor nutrition resulting to poor performance and injuries because they strive to achieve a lower body weight or low body fat content which may increase the risk of amenorrhea, negative energy balance and eating disorders consequently affecting performance negatively. A good diet helps support consistent intensive training while reducing the risk of illness or injury, improving recovery time and maximizing exercise performance Burke et al., (2001) [4] . Therefore, adopting the required practice of sports nutrition would help develop sound eating habits for optimal athletic performance and provide lifetime health benefits for the athletes. Therefore this study was designed to evaluate the association of performance and injury with dietary intake and nutritional status engaged in regular practice in a training camp.

Association Between Performance And Injury With Dietary Intake And Nutritional Status Among DOI: 10.9790/6737-04036271 www.iosrjournals.org 63 | Page II. Study Design And Methodology 2.1 Study Design A cross sectional study design was used where 282 athletes were selected randomly and proportionate to size sampling allocated to two groups of males (192) and females (90). Each group was divided into middle and long distance runners with the option that one individual could belong to the two categories. The study population comprises of athletes training in Iten training camp including both elite and amateur, middle distance athletes (3000meters, 5000meters, and 10000meters) and long distance (21kilometers and 42kilometers).The sample size calculation was based on the formula by Fischer’s et al., (1991) [5] with modification for a population less than 10,000 at 95% confidence interval. 2.2 Study Setting The study was conducted in Iten training camp, situated in Elgeyo Marakwet County. This is shown in Figure 1. Elgeyo Marakwet County is located in the former Rift valley province. Iten training camp is situated within Iten Township. The camp accommodates around 1500 runners. Iten is at altitude of 2400m above the sea level. Elgeyo Marakwet County has a population of 369,998 with a population density of 122 per km 2 and the tribes residing in the county are Keiyos and Marakwets KNBS, (2009) [6] . Figure 1: Elgeyo Marakwet county Map III. Data Collection A pre-tested semi structured questionnaire was used to collect data from the athletes. The questionnaire was structured to contain 24-hour recall, individual 24 hour dietary diversity score and anthropometry. A 24 hour recall questionnaire was administered to determine dietary intake of energy, calcium and iron. Measurement of food weights and/or the volume equivalent of foods were performed using kitchen diet scale and model utensils including cups, spoons, plates, measuring jars and cooking pans. The national food composition Tables Sehmi, (1993) [7] for Kenya, and the Nutri – survey software were used to compute the energy, calcium and iron consumed by individual athlete within the last 24 hours. Bathroom scales (seca) were used for taking weights and stadiometers (seca) were used to

and model utensils including cups, spoons, plates, measuring jars and cooking pans. The national food composition Tables Sehmi, (1993) [7] for Kenya, and the Nutri – survey software were used to compute the energy, calcium and iron consumed by individual athlete within the last 24 hours. Bathroom scales (seca) were used for taking weights and stadiometers (seca) were used to take heights which were used to calculate BMI as a measure of nutritional status. The weighing scales were calibrated regularly to ensure accuracy in taking readings. Performance of athletes was evaluated by comparing world record for the event with the best performance achieved and the differences were graded in terms of time taken for each race. Individual Dietary Diversity scores were determining the numbers of food groups consumed from among the athletes (FAO list of recommended food groups). Data was collected through the assistance of research enumerators who had been trained on the use the study tools. Pre testing of questionnaire was done in Chepkorio training camp in the same county but away from the study camp. This study was approved by the Kenyatta National Hospital/University of Nairobi Ethics and Research Committee and a research permit was obtained from the National Council Science Technology and Innovation (NACOSTI) in the Ministry of Higher Education, Science and Technology. Informed consent was obtained from individual athletes before the commencement of the study. Iten training camp

Association Between Performance And Injury With Dietary Intake And Nutritional Status Among DOI: 10.9790/6737-04036271 www.iosrjournals.org 64 | Page 3.1 Data handling and Statistical analysis Dietary intake of energy, calcium and iron were computed using Nutri- survey (2007) software and a Kenyan food composition Table Sehmi, (1993) [7] . Data was entered into a database using statistical package for social science SPSS (20.0). Data cleaning was done by running frequencies and using explores to check normalities, any missing data, outliers as well as consistency of responses between questions. Socio demographic and socio-economic data were analysed using descriptive statistics and simple frequencies, means and percentages. Chi-square test was used to determine association between variables and the statistical significance concept. Logistic regression analysis was used to calculate odds ratios and linear regression was used to determine the association between variables. The level of significance was set at P < 0.05. IV. Results 4.1 Socio Demographic Characteristics A total of 282 athletes were assessed in the study with 192 males and 90 females. Most of male athletes were between ages 25 -30 years while most of female athletes were between ages 18 – 24 years. Over 60% of both males and females were single. Majority (97.8%) of athletes attained at least primary education. With regards to ethnicity, majority of athletes were from the Kalenjin ethnicity. The results are shown in Table 1. Table 1: Socio Demographic Characteristics of Athletes Socio Demographic characteristics Male (n = 192) Female (n = 90) Frequency Percentage Frequency Percentage Age of athletes (years) 18 – 24 90 46.9 56 61.2 25 – 30 102 53.1 34 37.8 Marital status Single 121 63.1 55 62.1 Married 71 36.9 35 38.9 Education level Never attended school 1 0.5 2 2.2 Primary level 66 34.4 29 32.2 Secondary level 116 60.4 50 55.6 Tertiary level 9 4.7 9 10.0 Ethnicity Kalenjin 160 83.3 73 81.1 Kikuyu 4 2.1 7 7.8 Turkana 11 5.7 0 0.0 Masai 7 3.7 1 1.1 Samburu 6 3.1 2 2.2 Kisii 3 1.6 4 4.4 Kamba 1 0.5 3 3.4 4.2 Socio Economic Characteristics of Athletes The

level 66 34.4 29 32.2 Secondary level 116 60.4 50 55.6 Tertiary level 9 4.7 9 10.0 Ethnicity Kalenjin 160 83.3 73 81.1 Kikuyu 4 2.1 7 7.8 Turkana 11 5.7 0 0.0 Masai 7 3.7 1 1.1 Samburu 6 3.1 2 2.2 Kisii 3 1.6 4 4.4 Kamba 1 0.5 3 3.4 4.2 Socio Economic Characteristics of Athletes The athletes were placed into the three socio–economic categories of the country as follows: higher class (70,000 KShs per month), middle class (20,000 to 70,000 KShs per month) and lower class (below 20,000KShs per month). There were slightly more females in the lower class than males, slightly more males in middle class than females and slightly more males in the higher class than females. These results are shown in Table 2. It was indicated that apart from athletics, majority of the athletes obtained their income from either from farming or successful business. A small number were employees of the armed forces of Kenya. Table 2: Socio Economic Characteristics of Athletes Socio Economic characteristics Male (n =192) Female (n = 90) Socio Economic class Frequency Percentage Frequency Percentage Lower class 75 39.1 41 45.6 Middle class 94 48.9 40 44.4 Higher class 23 11.9 9 10 *1 KShs =$US 0.01 4.3 Distribution of athletes by Type of Sex of Race The races were grouped into two races; middle distance (3000m, 5000m and 10,000m) and long distance 21km and 42km (also known as half marathon and full marathon respectively). In all the five races, by far men were the majority except in 5000m race. This was even more pronounced in the marathon races. These results are shown in Table 3.

Association Between Performance And Injury With Dietary Intake And Nutritional Status Among DOI: 10.9790/6737-04036271 www.iosrjournals.org 65 | Page Table 3: Distribution of the Athletes by Type of Race Type of race Males (n = 192) Females (n = 90) Middle distance Frequency Percentage Frequency Percentage 3000 m 13 6.8 9 10.0 5000m 9 4.7 14 15.6 10,000m 46 23.9 20 22.2 Long distance 21 Km 61 31.7 27 30.0 42 Km 63 32.8 20 22.2 4.4 Performance of athletes The performance was categorized into excellent, good, fair and poor. This was established through ranking against the world record for the event, the best performance achieved by individual athlete. Excellent performance was measured as difference between the best world record and the best record achieved by individual athlete record achieved is less or equal to one minute, Good performance; greater than one minute and less than three minutes, Fair performance; greater than three minutes and less or equal to five minutes and Poor performance; greater than five minutes. Of the male athletes 17.7% had excellent performance, slightly lower than the female athletes with 21.1%. More females had poor performance as compared to male athletes as shown in Table 4. Table 4: Distribution of Athletes by Performance Performance levels of athletes Male (n = 192) Female (n = 90) Frequency Percentage Frequency Percentage Excellent 34 17.7 19 21.1 Good 107 55.7 44 48.9 Fair 32 16.7 13 14.4 Poor 19 9.9 14 15.6 4.4.1 Hindrance to Expected Performance According to the athletes, major hindrances to expected performance were injury, running out of breath and fatigue. More male athletes experienced injury as a major hindrance to their expected performance compared to the female athletes. The hindrance to expected performance of most of the female athletes was running out of breath and fatigue as compared to male athletes. These results are shown in Table 5. Table 5: Hindrance to expected Performance Hindrance to expected performance Male (n = 192) Female (n = 90) Frequency Percentage Frequency Percentage Injury 147 76.6 32 35.6 Running out of breath 19 9.9 29 32.2 Fatigue 26 13.5 29

female athletes was running out of breath and fatigue as compared to male athletes. These results are shown in Table 5. Table 5: Hindrance to expected Performance Hindrance to expected performance Male (n = 192) Female (n = 90) Frequency Percentage Frequency Percentage Injury 147 76.6 32 35.6 Running out of breath 19 9.9 29 32.2 Fatigue 26 13.5 29 32.2 4.5 Common Injuries suffered by Athletes Injuries that were common to athletes included hamstring, tendon, muscle cramps and ankle injuries yet majority of the athletes assumed that it was normal to suffer from this common injuries. There was a very small difference between female and male who suffered from common injuries that affected their performance. More males suffered from hamstring while more females suffered from muscle cramps. These results are shown in Table 6. Table 6: Common Injuries suffered by Athletes Common injuries Male (n = 192) Female (n = 90) Frequency Percentage Frequency Percentage Hamstring 83 43.2 17 18.8 Tendon 32 16.7 19 21.1 Muscle cramps 37 19.3 39 43.3 Ankle 40 20.8 15 16.7 4.5.1 Association between Common Injuries and Sex A chi square test and odds ratio was used to determine the association between sex and injuries suffered by athletes. From the chi square test, the findings indicated that more females were at risk of getting injured than males with a P value of 0.002.Odds ratio indicated that females were 5.8 times more likely to suffer from injury than male athletes. These results are shown in Table 7.

Association Between Performance And Injury With Dietary Intake And Nutritional Status Among DOI: 10.9790/6737-04036271 www.iosrjournals.org 66 | Page Table 7: Association between common injuries and sex Experiencing injuries OR 95%CI P value Yes No Frequency % Frequency % Male (n=190) 160 83.3 32 16.7 5.8 1.726- 1.9487 0.002 * Female(n=92) 87 96.7 3 3.3 4.6 Dietary Intake of Energy, Iron and Calcium Significantly higher percentages of males met the RDA for the three nutrients than females. No female met the RDA for iron. The higher percentage that met the RDA for any of the three nutrients among males and females was about 35%.The results are shown in the Table8 Table 8: Dietary intake of Energy, Iron and Calcium Nutrient intake RDA Range of RDA % RDAs % meeting RDA or More P value Energy (Kcal) Male 2200 kcal 1800 – 2900kcal 81.8 – 131.8 34.8 0.002 * Female 2100kcal 1000-2200kcal 47.6 – 104.7 6.3 Iron Male 10mg 7 – 11mg 70 – 110.0 21.7 0.048 * Female 14mg 6 – 9mg 42.9- 64.3 0.0 Calcium Male 1000mg 700 – 1200 mg 70.0 – 120.0 30.4 0.042 * Female 1000mg 500 – 900mg 50.0 – 90.0 12.3 *Significant <0.05 4.6.1 Association between Performance and Dietary Intake of Energy, Iron, Calcium and Nutritional Status Linear regression model was used to compute the association between performance and dietary intake of energy, iron, calcium and nutritional status. The findings indicated a positive association with a 2 tailed sigma of < 0.05. These results are shown in Table 9. Table 9: Association between performance and Dietary Intake of Energy, Iron, Calcium and Nutritional Status ß R r 2 Sig Lower CI(ß) Upper CI(ß) Energy 1.186 0.611 0.373 0.002 * 0.507 1.865 Iron 0.486 0.515 0.266 0.001 * 0.224 0.748 Calcium 0.002 0.300 0.090 0.004 * 0.057 0.064 Nutritional status 0.588 0.561 0.315 0.000 * 0.486 0.690 4.7 Categories of foods consumed by athletes Figure 2: Categories of food groups consumed by athletes Fig 2 shows the food groups as classified by FAO, (2010). From the study male athletes had the highest percentage in consumption of

0.001 * 0.224 0.748 Calcium 0.002 0.300 0.090 0.004 * 0.057 0.064 Nutritional status 0.588 0.561 0.315 0.000 * 0.486 0.690 4.7 Categories of foods consumed by athletes Figure 2: Categories of food groups consumed by athletes Fig 2 shows the food groups as classified by FAO, (2010). From the study male athletes had the highest percentage in consumption of different food groups as compared to females with smaller percentage of consumption. Regression was used P 0.012 indicated that males had significantly higher consumption of the food groups than females. Starchy food had the highest consumption of 68.3% and 31.7% for males and females respectively. Consumption of milk and milk products which may have been consumed in tea, plain or fermented was also high. Fish, flesh meat, eggs and organ meat were the least among athletes.

Association Between Performance And Injury With Dietary Intake And Nutritional Status Among DOI: 10.9790/6737-04036271 www.iosrjournals.org 67 | Page 4.8 Individual Dietary Diversity Score based on 24- hour recall Individual dietary diversity score (IDDS) were used to evaluate the dietary practices in terms of the numbers of food groups consumed out of the FAO recommended. Dietary diversity scores were calculated as the number of food groups consumed over a period of 24-hours. FAO cut-off points were used to classify Dietary Diversity Scores. The mean IDDS for the athletes was 4.34 ± 0.45. Significantly higher percentages of males had higher IDDS than females while significantly higher percentages of females had moderate IDDS than males. No athlete from the two groups had low IDDS. These results are shown in Table10. Table 10: Individual Dietary Diversity Score Dietary diversity score Males (n = 192) Females (n = 90) χ 2 P value Frequency % Frequency % 1 – 3 Low 0 0.0 0 0.0 11.625 0.001 * 4 – 7 Moderate 119 62.0 74 82.2 8 and above High 73 38.0 16 17.8 χ 2 = Chi square * Significant <0.05 4.9 Nutritional Status of Athletes The nutritional status of athletes was measured as BMI. Almost 50% of the male athletes had normal BMI as compared to 31.1% of the female athletes. More female athletes at 5.6% were severely underweight compared to male athletes at 1.6%. None of the athletes was above normal BMI with the highest having BMI of 22Kgs/M 2 . A chi square test was done with P value of 0.047 indicating that majority of the female athletes were underweight compared to male athletes. And odds ratio was done showing that female athletes were 0.701 times more likely to be underweight than male athletes. These results are shown in Table 11. Table 11: Nutritional Status of Athletes Nutritional status Cut off BMI (Kg/M 2 ) Male (n = 192) Female(n= 90) χ 2 P value Frequency % Frequency % Severely underweight <16 3 1.6 5 5.6 7.958 0.047 * Moderately underweight 16.0-16.99 33 17.2 21 23.3 Mildly underweight 17.0-18.49 62 32.3