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Iron in polish female soccer players–dietary intake and questionnaire validation
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Science & Sports (2019) xxx, xxx.e1—xxx.e8
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ORIGINAL ARTICLE
Iron in polish female soccer players—dietary intake and questionnaire validation Le fer chez des footballeuses polonaises—apport alimentaire et validation de questionnaire H. Dobrowolski ∗, D. Włodarek Department of Dietetics, Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (SGGW), ul. Nowoursynowska 159C, 02-776 Warsaw, Poland Received 30 June 2019; accepted 23 September 2019
KEYWORDS Iron; Soccer; Women; Dietary intake; FFQ; Validation
MOTS CLÉS Fer ; Football ;
∗
Summary Objectives. — The aim of the study was to evaluate dietary iron intake by professional female soccer players and to estimate the possible risk of iron deficiency. Equipment and methods. — The research was completed by 38 professional soccer players of the three soccer leagues: Ekstraleague, I League, and II League. The participants had their height and body mass measured. The data food consumption and iron intake was obtained through the method of a systematic recording of results conducted throughout a 3-day long period and food frequency questionnaire, adapted to evaluation of intake of this particular micronutrients (IRONIC-FFQ). Results. — The age of the participants was 21 ± 5 years, the height was 167 ± 5 cm, and the body mass median was 59,2 kg. Most common iron sources in study group were cereal products (31,8%), meats (14,1%) and vegetables (9,9%). Iron intake with using IRONIC-FFQ method was 8,06 mg, while using 3-day dietary food record method was 8,8 mg. After comparing both method with each other we found a correlation (P < 0,001, r = 0,568) and Bland-Altman index of 7,89%. Conclusion. — In conclusion, iron intake in study group was sufficient. Validation of IRONIC-FFQ with Spearman test gives positive results, but failed with Bland-Altman plot. © 2019 Elsevier Masson SAS. All rights reserved.
Résumé Objectifs. — L’objectif de l’étude était d’évaluer des apports alimentaires en fer chez des footballeuses professionnelles et estimer des risques possibles de carence en fer. Matériels et méthodes. — La recherche a été portée sur 38 joueuses de trois ligues de football: Ekstraligue, Ligue 1 et Ligue 2. Les participantes ont eu leur taille et masse corporelle
Corresponding author. E-mail address: hubert [email protected] (H. Dobrowolski).
https://doi.org/10.1016/j.scispo.2019.09.003 0765-1597/© 2019 Elsevier Masson SAS. All rights reserved.
Please cite this article in press as: Dobrowolski H, Włodarek D. Iron in polish female soccer players—dietary intake and questionnaire validation. Sci sports (2019), https://doi.org/10.1016/j.scispo.2019.09.003
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Femmes ; Apport alimentaire ; QFCA ; Validation
H. Dobrowolski, D. Włodarek calculées. Les données concernant la consommation alimentaire et les apports en fer ont été obtenues au moyen d’enregistrement systématique de résultats, effectué pendant 3 jours et de questionnaire de fréquence de consommation des aliments, adapté à évaluer l’apport en ce micronutriment particulier (FER QFCA). Résultats. — L’âge de participantes était de 21 ± 5 ans, leur taille de 167 ± 4,7 cm et la médiane de leur masse corporelle de 59,2 kg. Les sources de fer les plus populaires chez le groupe d’essai étaient les suivantes: les produits de céréales (31,8 %), la viande (14,1 %), et les légumes (9,9 %). L’apport en fer, selon la méthode FER QFCA, était de 8,06 mg, pendant que, selon la méthode d’enregistrement d’alimentaire diététique pendant 3 jours, de 8,8 mg. Apres avoir comparé les deux méthodes, nous avons trouvé une corrélation (p < 0,001, r = 0,568) et l’index de Bland Altman de 7,89 %. Conclusion. — En conclusion, l’apport en fer chez le groupe d’essai était suffisant. La validation de FER QFCA avec le test de Spearman donne des résultats positifs mais a échoué avec le graphique de Bland Altman. © 2019 Elsevier Masson SAS. Tous droits r´ eserv´ es.
1. Introduction
2. Materials and methods
Iron is one of the key micronutrients in athletes diet. It is used by the human body to transport oxygen and produce energy [1], which definitely have an impact on athletes’ performance. Iron also influences on other biological functions, such as breathing, DNA synthesis and cells proliferation [2]. Proper dietary intake, absorption and cellular use of iron is crucial for endurance performance [3]. Low iron intake can cause reduction of exercise capacity. Anemia caused by iron deficiency can reduce physical performance, while depletion of iron stores adversely affects adaptations to aerobic training, increases muscle fatigability and decreases energetic efficiency during submaximal exercise [4]. Athletes are particularly vulnerable for iron deficiency and, therefore—anemia. Bleeding from the gastrointestinal tract, which is often observed in sportspeople population, hematuria induced by exercise, sweat loss during training, as well as training itself are major causes of iron loss in athletes [5]. Women are especially vulnerable for iron deficiencies in this group. The risk of iron deficiency is higher and observed more often [4—6]. It is caused, among other things, by menstrual cycle, which increases iron loss during menstruation [5]. It is worth to notice, that iron needs in group of sportswomen could be higher even to 70% of EAR [7]. DellaValle (2013) highlighted in her study, that the need for constant control of hemoglobin and ferritin level among female athletes who are at risk of the iron deficit at the beginning and during the training period. Also, she pointed the necessity of proper dietary recommendations and iron supplementation in these athletes, who showed the deficit [8]. Landahl et al. (2005) in turn showed, that iron deficiency is a common problem among female soccer teams [9]. There is therefore a well-founded need to monitor female soccer players in terms of the risk of iron deficiency and associated with it anemia. The aim of the study was to evaluate dietary iron intake by professional female soccer players training in different league level and to estimate the possible risk of iron deficiency.
The research was completed on the basis of 38 professional female soccer players playing in different league-level clubs (Ekstraleague, I League and II League; n = 9, n = 16, and n = 13, respectively; according to the Polish soccer league system: Ekstraliga, I Liga, and II Liga, respectively). Mean age of the players was 21 ± 5 years old (21 ± 3 years old; 19 ± 4 years old; 24 ± 5 years old for Ekstraleague, I League and II League, respectively). The research and all the procedures were approved by the local Ethics and Scientific Research on Humans Commission (approval number: 24/2017, June 19th, 2017). All participants or (if underage) their legal guardians consented to take part in the study, to have all the measurements taken, and undergo various tests during the research. Exclusion criteria was: long-term injuries (not permitted to train within the last 6 months), diagnosed with any long-term disease (especially diseases impacting iron metabolism), failed to submit or submitted incomplete questionnaires. Inclusion criteria: be under 35 years old, be registered in the local Soccer Association, and take an active part in training sessions. Before training session body mass and height were measured twice by means of a standard scale and stadiometer accurate to within 0,1 kg and 0,1 cm, respectively. The final result was computed on the basis of the arithmetic average of the measurements. During the body mass measurements, the participants were asked to wear only underwear; whereas during the height measurements, no footwear or socks were allowed. On the basis of height and weight measurements results, Body Mass Index were calculated. To assess dietary iron intake we used food frequency questionnaire, adapted to evaluation of intake of this particular micronutrients (IRONIC-FFQ). In questionnaire were questions about dietary intake of portions of various food product groups: meats, meat products, eggs, fishes, dairy products, cereal products, fruits, vegetables, potatoes, fats, nuts and seed and cocoa products. Particular products with determination of typical serving size and iron content in this products (used in IRONIC-FFQ) are presented
Please cite this article in press as: Dobrowolski H, Włodarek D. Iron in polish female soccer players—dietary intake and questionnaire validation. Sci sports (2019), https://doi.org/10.1016/j.scispo.2019.09.003
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Iron intake in female soccer players in Table 1 (tab. 1). Participants were asked to fill questionnaire with typical products intake on a weekly basis, as well as qualification of 5 most often consuming meat products. A questionnaire was validated before in group of young women in research conducted by Gł˛ abska et al. (2017) [10]. Additionally, to assess a dietary iron intake, the method of 3-day dietary food recording was used. Since the weekly training schedule of the participants included most of the weekdays, they completed the recording of 2 days of their training and 1 day without the training, during which there were no official league games. To assess the volume of the food portions participants used Album of Photographs of
xxx.e3 Food Products and Dishes (in polish: Album fotografii produktów i potraw) [11] and website presenting a great database of pictures of products presented in various combinations with their mass (in cups, on plates, in the palm of the hand, etc.), which in turn helps the users asses the mass of a particular consumed product and dishes [12]. The amount of iron consumed were estimated by means of the computer ˙Z ˙, Poland). Results obtained by both program Dieta 5.0 (IZ methods were compared with national recommendations [13] on EAR level, which are 8 mg/day in study group. The statistical analysis was conducted by means of the SPSS v. 20 software (IBM Corp., USA). To verify the
Table 1 The IRONIC-FFQ questionnaire (information about the iron quantity in the serving was not place in the IRONIC-FFQ). Table was copied from Gł˛ abska et al. (2017) research [10]. Group of Products Products
Serving Size
Iron Content/Serving (mg)
Meat
100 g (palm of small hand)
13.30
100 g (palm of small hand) 100 g (palm of small hand) 100 g (palm of small hand) 100 g (palm of small hand) 250 g (1 glass) 25 g (e.g., 1/2 of wiener, medium ham, 5 slices of sausage) 25 g (e.g., 1/2 of wiener, medium ham, 5 slices of sausage) 25 g (e.g., 1/2 of wiener, medium ham, 5 slices of sausage) 25 g (e.g., 1/2 of wiener, medium ham, 5 slices of sausage) 50 g (1 egg) 50 g (deck of cards) 50 g (deck of cards) 250 g (1 glass)
slice of
3.30 2.60 1.00 1.00 0.25 4.22
slice of
1.35
slice of
0.21
slice of
0.48
Meat products
Liver (pork, beef, calf, poultry), pork kidney Other pork offal, poultry stomach Beef, calf, lamb, horse, goose, duck meat Pork meat Poultry meat Broth Blood pudding sausage Other offal cold cuts Loin cold cuts, ham, poultry sausages Other sausages, wiener, smoked gammon, spam, pate, salami, brawn cold cut, bacon
Eggs Fish Dairy products
Cereal products
Fruits Vegetables Potatoes Fats Nuts and seeds Cocoa products
Sardines Other fish and fish products Milk and milk beverages (yoghurt, kefir, buttermilk, cream) Cottage cheese Rennet and processed cheese White wheat and rye bread, bakery wares Dark bread, wholemeal, with grains, graham bread, pumpernickel bread Crispbread Wheat bran, wheat germs Iron-fortified corn flakes and cereals Other cereal products (uncooked) Fresh fruits Dried fruits Dry legumes Other vegetables
Poppy, pumpkin and flaxseed Other nuts and seeds Cocoa Chocolate
50 g 25 g 35 g 35 g
(1 thick slice, 2 tablespoons) (1 slice, 1 triangle serving) (1 slice, small roll) (1 slice, small roll)
10 g (1 slice) 10 g (1 spoon) 35 g (1 glass) 100 g (e.g., 1 glass of pasta or oatmeal, 1/2 glass of rice or groats) 100 g (1 medium piece, 1 glass) 50 g (handful) 100 g (1/2 of glass) 100 g (1 medium piece, 1 glass) 100 g (1 large piece) 10 g (1 spoon) 30 g (handful, 3 spoons of seeds) 30 g (handful, 3 spoons of seeds) 10 g (1 spoon) 20 g (1/5 of bar)
1.10 1.07 0.45 0.37 0.10 0.15 0.37 0.70 0.40 1.20 4.30 2.70 0.65 1.28 6.80 1.10 0.50 0.20 3.78 1.28 1.07 0.41
Please cite this article in press as: Dobrowolski H, Włodarek D. Iron in polish female soccer players—dietary intake and questionnaire validation. Sci sports (2019), https://doi.org/10.1016/j.scispo.2019.09.003
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xxx.e4 Table 2
H. Dobrowolski, D. Włodarek The characteristics of the research group.
Age [years]
Height [cm]
Body mass [kg]
BMI [kg/m2 ]
II league (mean ± SD min—max median)
I league (mean ± SD min—max median)
Ekstraleague (mean ± SD min—max median)
Overall (mean ± SD min—max median)
21 ± 3 17—25 19 167 ± 1 162—173 165 58,6 ± 6,4 49,3—68,4 57,8 21,1 ± 2,3 18—24,9 21,1
19 ± 4 13—27 19 167 ± 4 160—176 168 64,6 ± 12,8 49,1—94 60,2 22,9 ± 3,7 17,6—31,2 22
24 ± 5 17—31 25 168 ± 6 156—180 167 62,5 ± 6,8 54,1—77,2 59,5 22,1 ± 1,9 19,6—25,3 22,2
21 ± 5 13—31 21 167 ± 5 156—180 167 62,5 ± 9,8 49,1—94 59,2 22,2 ± 2,9 17,6—31,2 21,7
Figure 1 The structure of consumption of individual product groups, according to the amount consumed [g]. Chart presents median, because of non-parametric distribution (P < 0,05; Shapiro-Wilk test).
normality of distribution, the Shapiro-Wilk test was used. The ANOVA and Kruskal-Wallis test (in case of parametric and non-parametric values, respectively) were applied in order to compare the amount of products and iron consumed between different teams. The correlation was based on the Spearman’s test in terms of non-parametric values. To compare both methods (3-day dietary food record and IRONIC-FFQ) Spearman‘s correlation test and Bland-Altman index were used. To evaluate usefulness of IRONIC-FFQ test a sensitivity and specificity were calculated. The Receiver Operator Characteristic (ROC) curve was made to evaluate sensitivity and specificity and to select the best cut-off score of the questionnaire. The study’s defined significance level was set to ␣ = 0,05.
3. Results The results of anthropometric measurements are presented in Table 2. Body mass median of players were
59,2 kg (49,1—94 kg), medium height were 167 ± 5 cm and body mass index median in our group were 21,7 kg/m2 (17,6—31,2 kg/m2 ). The consumption of various products groups are presented in chart (Fig. 1). The highest amount of meats, fishes, fruits, vegetables, potatoes, nuts and seed were consumed by I-league team (750 g/week, 50 g/week, 675 g/week, 550 g/week, 250 g/week and 60 g/week, respectively). Ekstraleague team were consuming highest amount of meat products, dairy products and cereal products (175 g/week, 1000 g/week and 690 g/week, respectively). II-league team, in turn, were consuming highest amount of fats (40 g/week). II-league and I-league teams were consuming same amounts of eggs and cocoa products (150 g/week and 40 g/week, respectively) and it was higher than ekstraleague team’s consumption (100 g/week and 20 g/week, respectively). No correlations between consumption of particular food products groups and body mass and BMI were found (P > 0,05, Spearman test).
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Iron intake in female soccer players
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Table 3 Contribution of various food products groups in daily dietary iron intake [%]. Product group
Mean ± SD
Median
Min—Max
Meat Meat products Eggs Fish Dairy products Cereal products Fruits Vegetables Potatoes Fats Nuts and seeds Cocoa products
19,97 ± 15,9 7,6 ± 12,2 7,6 ± 9,4 0,8 ± 1,5 3 ± 3,2 30 ± 14,6 7,8 ± 6 11,5 ± 10 2,3 ± 3,2 0,7 ± 1,1 6,1 ± 10,2 3,1 ± 6,8
14,1 3,5 5,8 0 2,1 31,8 6,4 9,9 1,2 0,1 1,7 1,1
0—72,1 0—70,1 0—42,5 0—7,6 0—17,1 0—72 0—30 0—35,5 0—13 0—4,1 0—41 0—40,6
Non-poarametric distribution (P > 0,05, Shapiro—Wilk test).
Table 3 are presenting contribution of various food products groups in daily dietary iron intake (Table 3). The most common source of iron in participants’ diet were cereal products, which deliver 31,8% (0—72%) of dietary iron. Next food group were meats (14,1%; 0—72,1%) and vegetables (9,9%; 0—35,5%). Lowest amounts of iron from diet were supplied by fishes (0%; 0—7,6%), fats (0,14%; 0—4,1%) and cocoa products (1,1%; 0—40,6%). Dietary iron intake calculated using IRONIC-FFQ method, as well as 3-day dietary food record and fulfillment of nutritional needs of iron were presented in Table 4 (Table 4). Median of iron intake in study group calculated using IRONICFFQ method was 8,06 mg (1,11—20,4 mg), while using 3-day dietary food record method was 8,8 mg (2,18—23,7 mg). Average iron intake obtained by questionnaire meant fulfillment of nutritional needs on 100,78% (13,8—255,4%) level, when intake obtained by food record—on 110,2% (27,3—296,8%) level. Nineteen players (46,3%) did not fulfill their needs from national recommendations with questionnaire method, while with use of dietary record - only 16 (39%). According to questionnaire—highest iron intake
Table 4
Figure 2 Bland-Altman plot comparing the IRONIC-FFQ with the 3-day dietary record iron intake.
was observed in I-league team (9,63 mg; 1,56—14,86 mg), and according to 3-day dietary food record—in II-league team (9,25 mg; 2,18—15,13 mg). No correlation between age, height, body mass, BMI, iron intake and fulfillment of nutritional needs (with use of both methods) were found (P > 0,05, Spearman test). No statistically significant differences in iron intake between different teams, on the basis of 3-day dietary food record, were found (P = 0,957, KruskalWallis test). We did, however, find significant difference in iron intake between teams on the basis of IRONIC-FFQ—IIleague team consumed higher amount of iron than I-league team (P = 0,013, Kruskal-Wallis test). After comparing both method with each other (IRONICFFQ vs. 3-day dietary food record) we found a strong correlation (P < 0,001, r = 0,568, Spearman test). Fig. 2 present the comparison of both method with use of BlandAltman plot. The mean absolute difference in both methods was −0,7593 with standard deviation 3,09128. This, in turn, allowed us to set lower agreement limit and upper agree-
Dietary iron intake and fulfillment of nutritional needs.
Iron intake
IRONIC-FFQ
Iron intake
3-day dietary record
Needs fulfillment
IRONIC-FFQ
Needs fulfillment3-day dietary record
II league (mean ± SD median min—max)
I league (mean ± SD median min—max)
Ekstraleague (mean ± SD median min—max)
Overall (mean ± SD median min—max)
5,73 ± 3,1 5,91 1,11—10,6 8,93 ± 4,3 9,25 2,18—15,1 71,6 ± 33 73,9 13,8—132,1 111,7 ± 53,8 115,6 27,3—189,2
10,3 ± 3,7 9,63 5,87—20,4 9,61 ± 4,6 8,12 4,82—23,7 129 ± 46,3 120,4 73,4—255,4 120,2 ± 57,5 101,4 60,2—296,8
7,78 ± 3,5 7,88 1,57—14,9 8,65 ± 2,4 8,83 4,37—12,5 97,2 ± 44,1 98,5 19,6—185,7 108,2 ± 47,8 110,4 54,6—156,6
8,37 ± 3,9 8,06 1,11—20,4 9,1 ± 3,8 8,8 2,18—23,7 104,6 ± 48,7 100,8 13,8—255,4 114,1 ± 47,8 110,2 27,3—296,8
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H. Dobrowolski, D. Włodarek
Figure 3
The ROC curve.
ment limit on level of −6,81821 and 5,299609, respectively. The number of individuals observed to be beyond the LOA value was 3 out of 38, which meant Bland-Altman index of 7,89%, and 92,11% of compared results were in acceptable range ±2SD. Sensitivity of the IRONIC-FFQ was 75% and specificity was 68,2%. The ROC curve are presented on Fig. 3. The AUC was 0,835 and was statistically significant (P < 0,001). The ROC curve showed, that after achieving iron intake equal to 9,78 mg/day a sensitivity of IRONIC-FFQ is 100% (with 1specificity of 54,5%), while specificity is 100% if iron intake is lower than or equal to 4,89 mg/day (with sensitivity of 37,5%).
4. Discussion To the best of our knowledge, ours was the first research of this kind with use of IRONIC-FFQ to check iron intake level in female soccer players group. Our research is also the first, which compare two different method of iron intake assessment: 3-day dietary food record and IRONIC-FFQ—in female athlete population, in this case—professional soccer players. The IRONIC-FFQ results pointed to high intake of milk and dairy products (700 g/week; 0—3863 g/week), meats (750 g/week; 0—3250 g/week), cereal products (664 g/week; 0—1610 g/week) and fruits and vegetables (500 g/week; 0—3000 g/week and 400 g/week; 0—2000 g/week, respectively). Products with higher protein content (meat and dairy products) were consumed in highest amounts. Although athletes have increased nutritional needs of proteins, the diet should be based on carbohydrates, which are main source for generating energy necessary during training and competition. Well-balanced athletes’ diet should be then rich in cereal products (bread, rice, groats, pasta) and vegetables and fruits. After this, diet should be rich in milk and dairy products, eggs, meat and meat products. Nutritional plan can be sufficient to
fulfill athlete special need, when it is based on carbohydrate rich products, both simple and complex, depending on the current needs and the periodization of training. On lower amounts fishes (0 g/week; 0—350 g/week), fats (5 g/week; 0—210 g/week) and nuts and seeds (30 g/week; 0—240 g/week) were consumed. Fishes are good source of vitamin D, which insufficient intake are widely observed [14], and which low concentration is related to lower bone density, cancer, heart diseases, autoimmunity, infections and higher death risk [15]. Also fats, mostly vegetable fats, was consumed in lower amounts. Although proper amount of fats intake are highly possible in study group, because of high meat and high-fat dairy products consumption, it is worth to consider, if lower consumption of vegetable fats in favor to animal fats is really good nutritional solution. Most common iron sources in study group were cereal products (31,8%), meats (14,1%) and vegetables (9,9%). Considering the above in the context of total products consumed, it should be noted, that the dairy products, which were consumed in the largest amounts, are not rich in iron, so they were not made such an impact on overall iron content in the study group‘s diet. Most of the iron was delivered with cereal products. Highest amounts of iron in cereal products are found primarily in flours, grains, groats, rice, pasta, cereals, crisps, and in cornflakes fortified with iron. Cereal products were food product group which were consumed in 3rd place, if consumed amounts will be taken into account. It is need to be pointed, that meats, which were consumed in higher amount and are a good iron source, delivered less iron than cereal products. It meant, that our group in their diet consumed cereal products which were good iron source and bypassing products such as bread, or bran and wheat germ, which are not good source of this micronutrient. Meat, in turn, which was consumed theoretically in smaller amount than cereal product, probably were consumed in products which contain less iron, such as pork, poultry, or meat broth. Analyzing the situation, where 3rd iron source in study group were vegetables, it must be said, that legumes are good iron source, which in the content of this ingredient can not only match meat products, but in some cases may even exceed it. Vegetables, however, are source of non-heme iron, which is worse absorbed in the body. Dietary iron intake calculated with both methods, IRONIC-FFQ and 3-day dietary food record, exceeded the EAR norms. Given the importance of iron in numerous biological functions and in athlete diet, the proper iron intake is crucial to maximize physical performance. It is worth to notice, however, that results obtained in our study are definitely lower than those obtained in other studies. Mullinix et al. (2003) in their research showed dietary iron intake on 16 ± 7,8 mg/day level, and this intake was still without minerals dietary supplements taken into account [16]. Also, Clark et al. (2003) in their study on female soccer players before and after soccer season got the results of 17,3 ± 4,7 mg/day and 12,2 ± 5,2 mg/day (for pre- and post-season, respectively) [17]. Even junior female soccer players from Canada consumed higher amount of iron with diet—16,2 ± 5,9 mg/day [18]. Study of Martin et al. (2006) showed, that group of female soccer players consumed 12,1 ± 6 mg of iron per day [19]. Studies in groups of women participating in other sports give inconclusive results.
Please cite this article in press as: Dobrowolski H, Włodarek D. Iron in polish female soccer players—dietary intake and questionnaire validation. Sci sports (2019), https://doi.org/10.1016/j.scispo.2019.09.003
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Iron intake in female soccer players Volleyball players in one research had similar iron intake [20] and higher than the results in our research [21]. What is interesting, one research conducted with women practicing endurance and aesthetic sports (runners, swimmers, cyclists, basketball players, bodybuilders tennis players and triathlonists) with subclinical eating disorder showed higher iron intake (17,6 ± 6,2 mg/day) than observed in our study [22]. Therefore, comparing our results of dietary iron intake with results obtained in studies of other authors we must apply, that despite iron intake was high enough in our study, the consumption was relatively low. It could turn out to be insufficient, especially because how it was pointed in introduction, female athletes nutritional needs for iron may be much more higher, than those with lower physical activity level [7]. In that context, iron concentration and serum ferritin level control seems to be necessary. Spearman‘s test showed correlation coefficient R = 0,568. Masson et al. (2003) stated, that validity of FFQ may be confirmed by correlation coefficient higher than 0,5 [23]. Gł˛ abska et al. in their work compared both methods used in this study on group of young women with getting correlation coefficient R = 0,48 and rightly pointed, that they can’t clearly reject validation results because of almost achieved correlation coefficient, just how it was done in other questionnaires [10]. It must be pointed, that in our study we achieved correlation coefficient above 0,5. It means, that IRONIQ-FFQ test is valid in the group of female soccer players and it can be used to assess the iron intake. It is the first questionnaire of the intake of dietary iron which can be conducted in group of female soccer players. The sensitivity of the IRONIC-FFQ test was 75%, which is ability of questionnaire to predict a low iron intake in group of female athletes. The 68,2% of specificity of the IRONIC-FFQ, in turn, pointed positive prediction of proper iron intake in study group. The higher ability of prediction low iron intake than proper iron intake indicates, that this questionnaire is more able to detect lower iron intake, which is crucial to detect, than to predict a higher intakes value. The AUC presented in and ROC curve was 0,835, which means that the IRONIC-FFQ is a moderate test, with proper sensitivity and specificity, to detect low iron intake. It is natural, that ROC curve should be as close to upper left corner of chart as possible, which would mean and sensitivity at 100% ratio (the true-positive rate) and 1-specificity at 0% (the false-positive rate). Our ROC curve present a best cut-off point at the 0,75 point in sensitivity and 0,18 at 1-specitficity when iron intake is 7,32 mg/day. It means, that at this level of intake the IRONIC-FFQ shows a 75% chance to detect insufficient iron intake and only 18% chance to show low iron intake, when its indeed proper. Despite above, used in this study Bland-Altman index did not give results which can confirm IRONIC-FFQ as positively validated. Bland-Altman plot is a ‘‘gold standard’’ in validation process [24] and it shows positive results, when they are lower than 5% [25]. Our results in this study were on 7,89% level and were not low enough. It is, however, very close to reference value. Despite the properly selected methods, some limitations of this study must be indicated. Those limitation could also be a reason why we do not achieve a positive validation results by using Blant-Altman plot, despite high results. First worth mentioning is that study group of 38 female soccer
xxx.e7 players could not be sufficiently large to conduct validation process of questionnaire. Second, our research was conducted in group of age 13 to 31 years old, which may be too big age shoot, especially with that size of a group. On the other hand, we did not want to eliminate part of participants of the study, because we wanted to obtained iron intake results and nutritional needs realization for whole team to compare results on teams of different league levels. Eliminations of some study participants to lower age shoot was indeed impossible, because it would give us wrong picture of whole team. In conclusion, iron intake in study group was sufficient. However, the national recommendations was barely exceeded, so taken into account statement, that EAR recommendations cannot be high enough, the iron concentration and serum ferritin level control seems to be justified, especially in half of study group which did not met the EAR level and part of group who barely exceeded it. Our results obtained with IRONIC-FFQ in study group must not give reliable results, with failed validation taken into account. However, taking into account the positive validation in the Gł˛ abska et al. study, Blant-Altman plot results nearly reference value and correlation coefficient on R = 0,568 level, we must say that results of IRONIC-FFQ utility in women athletes population are promising. The ROC curve showed a good sensitivity and specificity of this questionnaire at low iron intake level, which shows that it is useful tool in detection of low iron intake level. More tests with this questionnaire on larger groups and/or with lower age shoot and in different sports disciplines should be conducted. In our opinion, according to results achieved in our study, the IRONIC-FFQ can be useful tool in studies for measurement iron intake level in athletes population.
Funding This research was funded by Polish Ministry of Science and Higher Education within funds of Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS), for scientific research.
Disclosure of interest The authors declare that they have no competing interest.
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H. Dobrowolski, D. Włodarek [17] Clark M, Reed DB, Crouse SF, Armstrong RB. Pre- and Postseason dietary intake, body composition, and performance indices of NCAA Division I Female Soccer Players. Int J Sport Nut Exerc Metabol 2003;13:303—19. [18] Gibson JC, Stuart-Hill L, Martin S, Gaul C. Nutrition status of junior elite Canadian female soccer athletes. Int J Sport Nut Exerc Metabol 2011;21:507—14. [19] Martin L, Lambeth A, Scott D. Nutritional practices of national female soccer players: analysis and recommendations. J Sports Sci Med 2006;5(1):130—7. [20] Papadopoulou SK, Papadopoulou SD, Gallos GK. Macro- and micro-nutrients intake of adolescent greek female volleyball players. Int J Sport Nutr Exerc Metab 2002;12:73—80. [21] Beals KA. Eating behaviors nutritional status and menstrual function in elite female adolescent volleyball players. J Am Dietetic Assoc 2002;102:1293—300. [22] Beals KA, Manore MM. Nutritional status of female athletes with subclinical eating disorders. J Am Dietetic Assoc 1998;98:419—25. [23] Masson LF, McNeill G, Tomany JO, Simpson JA, Peace HS, Wei L, et al. Statistical approaches for assessing the relative validity of a food-frequency questionnaire, use of correlation coefficients and kappa statistic. Public Health Nutr 2003;6:313—21. [24] Kowalkowska J, Slowinska MA, Slowinski D, Dlugosz A, Niedzwiecka E, Wadolowska L. Comparison of a full foodfrequency questionnaire with the three-day unweighted food records in young Polish adult women, impolications for dietary assessment. Nutrients 2013;19:2747—76. [25] Myles PS, Cui J. Using the Bland Altman method to measure agreement with repeated measures. Br. J. Anaesth 2007;99:309—11.
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ORIGINAL ARTICLE
Iron in polish female soccer players—dietary intake and questionnaire validation Le fer chez des footballeuses polonaises—apport alimentaire et validation de questionnaire H. Dobrowolski ∗, D. Włodarek Department of Dietetics, Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (SGGW), ul. Nowoursynowska 159C, 02-776 Warsaw, Poland Received 30 June 2019; accepted 23 September 2019
KEYWORDS Iron; Soccer; Women; Dietary intake; FFQ; Validation
MOTS CLÉS Fer ; Football ;
∗
Summary Objectives. — The aim of the study was to evaluate dietary iron intake by professional female soccer players and to estimate the possible risk of iron deficiency. Equipment and methods. — The research was completed by 38 professional soccer players of the three soccer leagues: Ekstraleague, I League, and II League. The participants had their height and body mass measured. The data food consumption and iron intake was obtained through the method of a systematic recording of results conducted throughout a 3-day long period and food frequency questionnaire, adapted to evaluation of intake of this particular micronutrients (IRONIC-FFQ). Results. — The age of the participants was 21 ± 5 years, the height was 167 ± 5 cm, and the body mass median was 59,2 kg. Most common iron sources in study group were cereal products (31,8%), meats (14,1%) and vegetables (9,9%). Iron intake with using IRONIC-FFQ method was 8,06 mg, while using 3-day dietary food record method was 8,8 mg. After comparing both method with each other we found a correlation (P < 0,001, r = 0,568) and Bland-Altman index of 7,89%. Conclusion. — In conclusion, iron intake in study group was sufficient. Validation of IRONIC-FFQ with Spearman test gives positive results, but failed with Bland-Altman plot. © 2019 Elsevier Masson SAS. All rights reserved.
Résumé Objectifs. — L’objectif de l’étude était d’évaluer des apports alimentaires en fer chez des footballeuses professionnelles et estimer des risques possibles de carence en fer. Matériels et méthodes. — La recherche a été portée sur 38 joueuses de trois ligues de football: Ekstraligue, Ligue 1 et Ligue 2. Les participantes ont eu leur taille et masse corporelle
Corresponding author. E-mail address: hubert [email protected] (H. Dobrowolski).
https://doi.org/10.1016/j.scispo.2019.09.003 0765-1597/© 2019 Elsevier Masson SAS. All rights reserved.
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Femmes ; Apport alimentaire ; QFCA ; Validation
H. Dobrowolski, D. Włodarek calculées. Les données concernant la consommation alimentaire et les apports en fer ont été obtenues au moyen d’enregistrement systématique de résultats, effectué pendant 3 jours et de questionnaire de fréquence de consommation des aliments, adapté à évaluer l’apport en ce micronutriment particulier (FER QFCA). Résultats. — L’âge de participantes était de 21 ± 5 ans, leur taille de 167 ± 4,7 cm et la médiane de leur masse corporelle de 59,2 kg. Les sources de fer les plus populaires chez le groupe d’essai étaient les suivantes: les produits de céréales (31,8 %), la viande (14,1 %), et les légumes (9,9 %). L’apport en fer, selon la méthode FER QFCA, était de 8,06 mg, pendant que, selon la méthode d’enregistrement d’alimentaire diététique pendant 3 jours, de 8,8 mg. Apres avoir comparé les deux méthodes, nous avons trouvé une corrélation (p < 0,001, r = 0,568) et l’index de Bland Altman de 7,89 %. Conclusion. — En conclusion, l’apport en fer chez le groupe d’essai était suffisant. La validation de FER QFCA avec le test de Spearman donne des résultats positifs mais a échoué avec le graphique de Bland Altman. © 2019 Elsevier Masson SAS. Tous droits r´ eserv´ es.
1. Introduction
2. Materials and methods
Iron is one of the key micronutrients in athletes diet. It is used by the human body to transport oxygen and produce energy [1], which definitely have an impact on athletes’ performance. Iron also influences on other biological functions, such as breathing, DNA synthesis and cells proliferation [2]. Proper dietary intake, absorption and cellular use of iron is crucial for endurance performance [3]. Low iron intake can cause reduction of exercise capacity. Anemia caused by iron deficiency can reduce physical performance, while depletion of iron stores adversely affects adaptations to aerobic training, increases muscle fatigability and decreases energetic efficiency during submaximal exercise [4]. Athletes are particularly vulnerable for iron deficiency and, therefore—anemia. Bleeding from the gastrointestinal tract, which is often observed in sportspeople population, hematuria induced by exercise, sweat loss during training, as well as training itself are major causes of iron loss in athletes [5]. Women are especially vulnerable for iron deficiencies in this group. The risk of iron deficiency is higher and observed more often [4—6]. It is caused, among other things, by menstrual cycle, which increases iron loss during menstruation [5]. It is worth to notice, that iron needs in group of sportswomen could be higher even to 70% of EAR [7]. DellaValle (2013) highlighted in her study, that the need for constant control of hemoglobin and ferritin level among female athletes who are at risk of the iron deficit at the beginning and during the training period. Also, she pointed the necessity of proper dietary recommendations and iron supplementation in these athletes, who showed the deficit [8]. Landahl et al. (2005) in turn showed, that iron deficiency is a common problem among female soccer teams [9]. There is therefore a well-founded need to monitor female soccer players in terms of the risk of iron deficiency and associated with it anemia. The aim of the study was to evaluate dietary iron intake by professional female soccer players training in different league level and to estimate the possible risk of iron deficiency.
The research was completed on the basis of 38 professional female soccer players playing in different league-level clubs (Ekstraleague, I League and II League; n = 9, n = 16, and n = 13, respectively; according to the Polish soccer league system: Ekstraliga, I Liga, and II Liga, respectively). Mean age of the players was 21 ± 5 years old (21 ± 3 years old; 19 ± 4 years old; 24 ± 5 years old for Ekstraleague, I League and II League, respectively). The research and all the procedures were approved by the local Ethics and Scientific Research on Humans Commission (approval number: 24/2017, June 19th, 2017). All participants or (if underage) their legal guardians consented to take part in the study, to have all the measurements taken, and undergo various tests during the research. Exclusion criteria was: long-term injuries (not permitted to train within the last 6 months), diagnosed with any long-term disease (especially diseases impacting iron metabolism), failed to submit or submitted incomplete questionnaires. Inclusion criteria: be under 35 years old, be registered in the local Soccer Association, and take an active part in training sessions. Before training session body mass and height were measured twice by means of a standard scale and stadiometer accurate to within 0,1 kg and 0,1 cm, respectively. The final result was computed on the basis of the arithmetic average of the measurements. During the body mass measurements, the participants were asked to wear only underwear; whereas during the height measurements, no footwear or socks were allowed. On the basis of height and weight measurements results, Body Mass Index were calculated. To assess dietary iron intake we used food frequency questionnaire, adapted to evaluation of intake of this particular micronutrients (IRONIC-FFQ). In questionnaire were questions about dietary intake of portions of various food product groups: meats, meat products, eggs, fishes, dairy products, cereal products, fruits, vegetables, potatoes, fats, nuts and seed and cocoa products. Particular products with determination of typical serving size and iron content in this products (used in IRONIC-FFQ) are presented
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Iron intake in female soccer players in Table 1 (tab. 1). Participants were asked to fill questionnaire with typical products intake on a weekly basis, as well as qualification of 5 most often consuming meat products. A questionnaire was validated before in group of young women in research conducted by Gł˛ abska et al. (2017) [10]. Additionally, to assess a dietary iron intake, the method of 3-day dietary food recording was used. Since the weekly training schedule of the participants included most of the weekdays, they completed the recording of 2 days of their training and 1 day without the training, during which there were no official league games. To assess the volume of the food portions participants used Album of Photographs of
xxx.e3 Food Products and Dishes (in polish: Album fotografii produktów i potraw) [11] and website presenting a great database of pictures of products presented in various combinations with their mass (in cups, on plates, in the palm of the hand, etc.), which in turn helps the users asses the mass of a particular consumed product and dishes [12]. The amount of iron consumed were estimated by means of the computer ˙Z ˙, Poland). Results obtained by both program Dieta 5.0 (IZ methods were compared with national recommendations [13] on EAR level, which are 8 mg/day in study group. The statistical analysis was conducted by means of the SPSS v. 20 software (IBM Corp., USA). To verify the
Table 1 The IRONIC-FFQ questionnaire (information about the iron quantity in the serving was not place in the IRONIC-FFQ). Table was copied from Gł˛ abska et al. (2017) research [10]. Group of Products Products
Serving Size
Iron Content/Serving (mg)
Meat
100 g (palm of small hand)
13.30
100 g (palm of small hand) 100 g (palm of small hand) 100 g (palm of small hand) 100 g (palm of small hand) 250 g (1 glass) 25 g (e.g., 1/2 of wiener, medium ham, 5 slices of sausage) 25 g (e.g., 1/2 of wiener, medium ham, 5 slices of sausage) 25 g (e.g., 1/2 of wiener, medium ham, 5 slices of sausage) 25 g (e.g., 1/2 of wiener, medium ham, 5 slices of sausage) 50 g (1 egg) 50 g (deck of cards) 50 g (deck of cards) 250 g (1 glass)
slice of
3.30 2.60 1.00 1.00 0.25 4.22
slice of
1.35
slice of
0.21
slice of
0.48
Meat products
Liver (pork, beef, calf, poultry), pork kidney Other pork offal, poultry stomach Beef, calf, lamb, horse, goose, duck meat Pork meat Poultry meat Broth Blood pudding sausage Other offal cold cuts Loin cold cuts, ham, poultry sausages Other sausages, wiener, smoked gammon, spam, pate, salami, brawn cold cut, bacon
Eggs Fish Dairy products
Cereal products
Fruits Vegetables Potatoes Fats Nuts and seeds Cocoa products
Sardines Other fish and fish products Milk and milk beverages (yoghurt, kefir, buttermilk, cream) Cottage cheese Rennet and processed cheese White wheat and rye bread, bakery wares Dark bread, wholemeal, with grains, graham bread, pumpernickel bread Crispbread Wheat bran, wheat germs Iron-fortified corn flakes and cereals Other cereal products (uncooked) Fresh fruits Dried fruits Dry legumes Other vegetables
Poppy, pumpkin and flaxseed Other nuts and seeds Cocoa Chocolate
50 g 25 g 35 g 35 g
(1 thick slice, 2 tablespoons) (1 slice, 1 triangle serving) (1 slice, small roll) (1 slice, small roll)
10 g (1 slice) 10 g (1 spoon) 35 g (1 glass) 100 g (e.g., 1 glass of pasta or oatmeal, 1/2 glass of rice or groats) 100 g (1 medium piece, 1 glass) 50 g (handful) 100 g (1/2 of glass) 100 g (1 medium piece, 1 glass) 100 g (1 large piece) 10 g (1 spoon) 30 g (handful, 3 spoons of seeds) 30 g (handful, 3 spoons of seeds) 10 g (1 spoon) 20 g (1/5 of bar)
1.10 1.07 0.45 0.37 0.10 0.15 0.37 0.70 0.40 1.20 4.30 2.70 0.65 1.28 6.80 1.10 0.50 0.20 3.78 1.28 1.07 0.41
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xxx.e4 Table 2
H. Dobrowolski, D. Włodarek The characteristics of the research group.
Age [years]
Height [cm]
Body mass [kg]
BMI [kg/m2 ]
II league (mean ± SD min—max median)
I league (mean ± SD min—max median)
Ekstraleague (mean ± SD min—max median)
Overall (mean ± SD min—max median)
21 ± 3 17—25 19 167 ± 1 162—173 165 58,6 ± 6,4 49,3—68,4 57,8 21,1 ± 2,3 18—24,9 21,1
19 ± 4 13—27 19 167 ± 4 160—176 168 64,6 ± 12,8 49,1—94 60,2 22,9 ± 3,7 17,6—31,2 22
24 ± 5 17—31 25 168 ± 6 156—180 167 62,5 ± 6,8 54,1—77,2 59,5 22,1 ± 1,9 19,6—25,3 22,2
21 ± 5 13—31 21 167 ± 5 156—180 167 62,5 ± 9,8 49,1—94 59,2 22,2 ± 2,9 17,6—31,2 21,7
Figure 1 The structure of consumption of individual product groups, according to the amount consumed [g]. Chart presents median, because of non-parametric distribution (P < 0,05; Shapiro-Wilk test).
normality of distribution, the Shapiro-Wilk test was used. The ANOVA and Kruskal-Wallis test (in case of parametric and non-parametric values, respectively) were applied in order to compare the amount of products and iron consumed between different teams. The correlation was based on the Spearman’s test in terms of non-parametric values. To compare both methods (3-day dietary food record and IRONIC-FFQ) Spearman‘s correlation test and Bland-Altman index were used. To evaluate usefulness of IRONIC-FFQ test a sensitivity and specificity were calculated. The Receiver Operator Characteristic (ROC) curve was made to evaluate sensitivity and specificity and to select the best cut-off score of the questionnaire. The study’s defined significance level was set to ␣ = 0,05.
3. Results The results of anthropometric measurements are presented in Table 2. Body mass median of players were
59,2 kg (49,1—94 kg), medium height were 167 ± 5 cm and body mass index median in our group were 21,7 kg/m2 (17,6—31,2 kg/m2 ). The consumption of various products groups are presented in chart (Fig. 1). The highest amount of meats, fishes, fruits, vegetables, potatoes, nuts and seed were consumed by I-league team (750 g/week, 50 g/week, 675 g/week, 550 g/week, 250 g/week and 60 g/week, respectively). Ekstraleague team were consuming highest amount of meat products, dairy products and cereal products (175 g/week, 1000 g/week and 690 g/week, respectively). II-league team, in turn, were consuming highest amount of fats (40 g/week). II-league and I-league teams were consuming same amounts of eggs and cocoa products (150 g/week and 40 g/week, respectively) and it was higher than ekstraleague team’s consumption (100 g/week and 20 g/week, respectively). No correlations between consumption of particular food products groups and body mass and BMI were found (P > 0,05, Spearman test).
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Iron intake in female soccer players
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Table 3 Contribution of various food products groups in daily dietary iron intake [%]. Product group
Mean ± SD
Median
Min—Max
Meat Meat products Eggs Fish Dairy products Cereal products Fruits Vegetables Potatoes Fats Nuts and seeds Cocoa products
19,97 ± 15,9 7,6 ± 12,2 7,6 ± 9,4 0,8 ± 1,5 3 ± 3,2 30 ± 14,6 7,8 ± 6 11,5 ± 10 2,3 ± 3,2 0,7 ± 1,1 6,1 ± 10,2 3,1 ± 6,8
14,1 3,5 5,8 0 2,1 31,8 6,4 9,9 1,2 0,1 1,7 1,1
0—72,1 0—70,1 0—42,5 0—7,6 0—17,1 0—72 0—30 0—35,5 0—13 0—4,1 0—41 0—40,6
Non-poarametric distribution (P > 0,05, Shapiro—Wilk test).
Table 3 are presenting contribution of various food products groups in daily dietary iron intake (Table 3). The most common source of iron in participants’ diet were cereal products, which deliver 31,8% (0—72%) of dietary iron. Next food group were meats (14,1%; 0—72,1%) and vegetables (9,9%; 0—35,5%). Lowest amounts of iron from diet were supplied by fishes (0%; 0—7,6%), fats (0,14%; 0—4,1%) and cocoa products (1,1%; 0—40,6%). Dietary iron intake calculated using IRONIC-FFQ method, as well as 3-day dietary food record and fulfillment of nutritional needs of iron were presented in Table 4 (Table 4). Median of iron intake in study group calculated using IRONICFFQ method was 8,06 mg (1,11—20,4 mg), while using 3-day dietary food record method was 8,8 mg (2,18—23,7 mg). Average iron intake obtained by questionnaire meant fulfillment of nutritional needs on 100,78% (13,8—255,4%) level, when intake obtained by food record—on 110,2% (27,3—296,8%) level. Nineteen players (46,3%) did not fulfill their needs from national recommendations with questionnaire method, while with use of dietary record - only 16 (39%). According to questionnaire—highest iron intake
Table 4
Figure 2 Bland-Altman plot comparing the IRONIC-FFQ with the 3-day dietary record iron intake.
was observed in I-league team (9,63 mg; 1,56—14,86 mg), and according to 3-day dietary food record—in II-league team (9,25 mg; 2,18—15,13 mg). No correlation between age, height, body mass, BMI, iron intake and fulfillment of nutritional needs (with use of both methods) were found (P > 0,05, Spearman test). No statistically significant differences in iron intake between different teams, on the basis of 3-day dietary food record, were found (P = 0,957, KruskalWallis test). We did, however, find significant difference in iron intake between teams on the basis of IRONIC-FFQ—IIleague team consumed higher amount of iron than I-league team (P = 0,013, Kruskal-Wallis test). After comparing both method with each other (IRONICFFQ vs. 3-day dietary food record) we found a strong correlation (P < 0,001, r = 0,568, Spearman test). Fig. 2 present the comparison of both method with use of BlandAltman plot. The mean absolute difference in both methods was −0,7593 with standard deviation 3,09128. This, in turn, allowed us to set lower agreement limit and upper agree-
Dietary iron intake and fulfillment of nutritional needs.
Iron intake
IRONIC-FFQ
Iron intake
3-day dietary record
Needs fulfillment
IRONIC-FFQ
Needs fulfillment3-day dietary record
II league (mean ± SD median min—max)
I league (mean ± SD median min—max)
Ekstraleague (mean ± SD median min—max)
Overall (mean ± SD median min—max)
5,73 ± 3,1 5,91 1,11—10,6 8,93 ± 4,3 9,25 2,18—15,1 71,6 ± 33 73,9 13,8—132,1 111,7 ± 53,8 115,6 27,3—189,2
10,3 ± 3,7 9,63 5,87—20,4 9,61 ± 4,6 8,12 4,82—23,7 129 ± 46,3 120,4 73,4—255,4 120,2 ± 57,5 101,4 60,2—296,8
7,78 ± 3,5 7,88 1,57—14,9 8,65 ± 2,4 8,83 4,37—12,5 97,2 ± 44,1 98,5 19,6—185,7 108,2 ± 47,8 110,4 54,6—156,6
8,37 ± 3,9 8,06 1,11—20,4 9,1 ± 3,8 8,8 2,18—23,7 104,6 ± 48,7 100,8 13,8—255,4 114,1 ± 47,8 110,2 27,3—296,8
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H. Dobrowolski, D. Włodarek
Figure 3
The ROC curve.
ment limit on level of −6,81821 and 5,299609, respectively. The number of individuals observed to be beyond the LOA value was 3 out of 38, which meant Bland-Altman index of 7,89%, and 92,11% of compared results were in acceptable range ±2SD. Sensitivity of the IRONIC-FFQ was 75% and specificity was 68,2%. The ROC curve are presented on Fig. 3. The AUC was 0,835 and was statistically significant (P < 0,001). The ROC curve showed, that after achieving iron intake equal to 9,78 mg/day a sensitivity of IRONIC-FFQ is 100% (with 1specificity of 54,5%), while specificity is 100% if iron intake is lower than or equal to 4,89 mg/day (with sensitivity of 37,5%).
4. Discussion To the best of our knowledge, ours was the first research of this kind with use of IRONIC-FFQ to check iron intake level in female soccer players group. Our research is also the first, which compare two different method of iron intake assessment: 3-day dietary food record and IRONIC-FFQ—in female athlete population, in this case—professional soccer players. The IRONIC-FFQ results pointed to high intake of milk and dairy products (700 g/week; 0—3863 g/week), meats (750 g/week; 0—3250 g/week), cereal products (664 g/week; 0—1610 g/week) and fruits and vegetables (500 g/week; 0—3000 g/week and 400 g/week; 0—2000 g/week, respectively). Products with higher protein content (meat and dairy products) were consumed in highest amounts. Although athletes have increased nutritional needs of proteins, the diet should be based on carbohydrates, which are main source for generating energy necessary during training and competition. Well-balanced athletes’ diet should be then rich in cereal products (bread, rice, groats, pasta) and vegetables and fruits. After this, diet should be rich in milk and dairy products, eggs, meat and meat products. Nutritional plan can be sufficient to
fulfill athlete special need, when it is based on carbohydrate rich products, both simple and complex, depending on the current needs and the periodization of training. On lower amounts fishes (0 g/week; 0—350 g/week), fats (5 g/week; 0—210 g/week) and nuts and seeds (30 g/week; 0—240 g/week) were consumed. Fishes are good source of vitamin D, which insufficient intake are widely observed [14], and which low concentration is related to lower bone density, cancer, heart diseases, autoimmunity, infections and higher death risk [15]. Also fats, mostly vegetable fats, was consumed in lower amounts. Although proper amount of fats intake are highly possible in study group, because of high meat and high-fat dairy products consumption, it is worth to consider, if lower consumption of vegetable fats in favor to animal fats is really good nutritional solution. Most common iron sources in study group were cereal products (31,8%), meats (14,1%) and vegetables (9,9%). Considering the above in the context of total products consumed, it should be noted, that the dairy products, which were consumed in the largest amounts, are not rich in iron, so they were not made such an impact on overall iron content in the study group‘s diet. Most of the iron was delivered with cereal products. Highest amounts of iron in cereal products are found primarily in flours, grains, groats, rice, pasta, cereals, crisps, and in cornflakes fortified with iron. Cereal products were food product group which were consumed in 3rd place, if consumed amounts will be taken into account. It is need to be pointed, that meats, which were consumed in higher amount and are a good iron source, delivered less iron than cereal products. It meant, that our group in their diet consumed cereal products which were good iron source and bypassing products such as bread, or bran and wheat germ, which are not good source of this micronutrient. Meat, in turn, which was consumed theoretically in smaller amount than cereal product, probably were consumed in products which contain less iron, such as pork, poultry, or meat broth. Analyzing the situation, where 3rd iron source in study group were vegetables, it must be said, that legumes are good iron source, which in the content of this ingredient can not only match meat products, but in some cases may even exceed it. Vegetables, however, are source of non-heme iron, which is worse absorbed in the body. Dietary iron intake calculated with both methods, IRONIC-FFQ and 3-day dietary food record, exceeded the EAR norms. Given the importance of iron in numerous biological functions and in athlete diet, the proper iron intake is crucial to maximize physical performance. It is worth to notice, however, that results obtained in our study are definitely lower than those obtained in other studies. Mullinix et al. (2003) in their research showed dietary iron intake on 16 ± 7,8 mg/day level, and this intake was still without minerals dietary supplements taken into account [16]. Also, Clark et al. (2003) in their study on female soccer players before and after soccer season got the results of 17,3 ± 4,7 mg/day and 12,2 ± 5,2 mg/day (for pre- and post-season, respectively) [17]. Even junior female soccer players from Canada consumed higher amount of iron with diet—16,2 ± 5,9 mg/day [18]. Study of Martin et al. (2006) showed, that group of female soccer players consumed 12,1 ± 6 mg of iron per day [19]. Studies in groups of women participating in other sports give inconclusive results.
Please cite this article in press as: Dobrowolski H, Włodarek D. Iron in polish female soccer players—dietary intake and questionnaire validation. Sci sports (2019), https://doi.org/10.1016/j.scispo.2019.09.003
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Iron intake in female soccer players Volleyball players in one research had similar iron intake [20] and higher than the results in our research [21]. What is interesting, one research conducted with women practicing endurance and aesthetic sports (runners, swimmers, cyclists, basketball players, bodybuilders tennis players and triathlonists) with subclinical eating disorder showed higher iron intake (17,6 ± 6,2 mg/day) than observed in our study [22]. Therefore, comparing our results of dietary iron intake with results obtained in studies of other authors we must apply, that despite iron intake was high enough in our study, the consumption was relatively low. It could turn out to be insufficient, especially because how it was pointed in introduction, female athletes nutritional needs for iron may be much more higher, than those with lower physical activity level [7]. In that context, iron concentration and serum ferritin level control seems to be necessary. Spearman‘s test showed correlation coefficient R = 0,568. Masson et al. (2003) stated, that validity of FFQ may be confirmed by correlation coefficient higher than 0,5 [23]. Gł˛ abska et al. in their work compared both methods used in this study on group of young women with getting correlation coefficient R = 0,48 and rightly pointed, that they can’t clearly reject validation results because of almost achieved correlation coefficient, just how it was done in other questionnaires [10]. It must be pointed, that in our study we achieved correlation coefficient above 0,5. It means, that IRONIQ-FFQ test is valid in the group of female soccer players and it can be used to assess the iron intake. It is the first questionnaire of the intake of dietary iron which can be conducted in group of female soccer players. The sensitivity of the IRONIC-FFQ test was 75%, which is ability of questionnaire to predict a low iron intake in group of female athletes. The 68,2% of specificity of the IRONIC-FFQ, in turn, pointed positive prediction of proper iron intake in study group. The higher ability of prediction low iron intake than proper iron intake indicates, that this questionnaire is more able to detect lower iron intake, which is crucial to detect, than to predict a higher intakes value. The AUC presented in and ROC curve was 0,835, which means that the IRONIC-FFQ is a moderate test, with proper sensitivity and specificity, to detect low iron intake. It is natural, that ROC curve should be as close to upper left corner of chart as possible, which would mean and sensitivity at 100% ratio (the true-positive rate) and 1-specificity at 0% (the false-positive rate). Our ROC curve present a best cut-off point at the 0,75 point in sensitivity and 0,18 at 1-specitficity when iron intake is 7,32 mg/day. It means, that at this level of intake the IRONIC-FFQ shows a 75% chance to detect insufficient iron intake and only 18% chance to show low iron intake, when its indeed proper. Despite above, used in this study Bland-Altman index did not give results which can confirm IRONIC-FFQ as positively validated. Bland-Altman plot is a ‘‘gold standard’’ in validation process [24] and it shows positive results, when they are lower than 5% [25]. Our results in this study were on 7,89% level and were not low enough. It is, however, very close to reference value. Despite the properly selected methods, some limitations of this study must be indicated. Those limitation could also be a reason why we do not achieve a positive validation results by using Blant-Altman plot, despite high results. First worth mentioning is that study group of 38 female soccer
xxx.e7 players could not be sufficiently large to conduct validation process of questionnaire. Second, our research was conducted in group of age 13 to 31 years old, which may be too big age shoot, especially with that size of a group. On the other hand, we did not want to eliminate part of participants of the study, because we wanted to obtained iron intake results and nutritional needs realization for whole team to compare results on teams of different league levels. Eliminations of some study participants to lower age shoot was indeed impossible, because it would give us wrong picture of whole team. In conclusion, iron intake in study group was sufficient. However, the national recommendations was barely exceeded, so taken into account statement, that EAR recommendations cannot be high enough, the iron concentration and serum ferritin level control seems to be justified, especially in half of study group which did not met the EAR level and part of group who barely exceeded it. Our results obtained with IRONIC-FFQ in study group must not give reliable results, with failed validation taken into account. However, taking into account the positive validation in the Gł˛ abska et al. study, Blant-Altman plot results nearly reference value and correlation coefficient on R = 0,568 level, we must say that results of IRONIC-FFQ utility in women athletes population are promising. The ROC curve showed a good sensitivity and specificity of this questionnaire at low iron intake level, which shows that it is useful tool in detection of low iron intake level. More tests with this questionnaire on larger groups and/or with lower age shoot and in different sports disciplines should be conducted. In our opinion, according to results achieved in our study, the IRONIC-FFQ can be useful tool in studies for measurement iron intake level in athletes population.
Funding This research was funded by Polish Ministry of Science and Higher Education within funds of Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS), for scientific research.
Disclosure of interest The authors declare that they have no competing interest.
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Please cite this article in press as: Dobrowolski H, Włodarek D. Iron in polish female soccer players—dietary intake and questionnaire validation. Sci sports (2019), https://doi.org/10.1016/j.scispo.2019.09.003