Dietary intake of professional Australian football athletes surrounding body composition assessment

Athletes were recruited from one club competing in the AFL by the sports dietitian. Athlete characteristics (i.e. age and playing experience) and body composition data are reported in Table 1. Body composition (i.e. body fat percentage (%BF), fat free mass (FFM), height and body mass) was assessed using gold standard measure Dual-energy X-ray absorptiometry (DXA), following a method previously described elsewhere [3, 17].

Dietary intake was assessed using 7-day estimated food diaries collected during the pre-season phase of the season where body composition assessment was taking place. A 7-day food diary was selected as this time period captures a typical training micro-cycle in this athlete cohort, which includes three main skills sessions and all other training modalities [2]. Specifically, the observation period included three main training days (containing one skills training session [60–70 min] and one weights session [50 min] per day), one light training day (one short skills training session [40 min], one short weights session [30 min] per day) and three recovery days (no scheduled activity). Data was collected using the ‘Easy Diet Diary’ App (n = 41), ‘WhatsApp’ (n = 4) and one written food diary. The ‘Easy Diet Diary’ App was selected as it uses a database of Australian foods, has been validated as a method of assessing dietary intake in groups and provides a more convenient way of recording dietary intake than conventional written methods [18, 19]. The application ‘WhatsApp’ was used for four participants who could not access ‘Easy Diet Diary’ due to logistical issues. Food diaries estimated intake of all foods, fluids and supplements consumed on these days. Dietary intake data was analysed using a nutrition composition software program (Foodworks Version 8, Xyris, Queensland). Average daily energy and macronutrient intakes were analysed against current American College of Sports Medicine (ACSM) and International Olympic Committee (IOC) recommendations [4, 20, 21]. Individual energy requirements were estimated using the Harris-Benedict equation, using activity factors that reflected training loads [22]. Activity factors were categorised according to main, light and recovery training days (i.e. main: 1.75, light: 1.55 and recovery: 1.375). Intake was assessed in terms of food groups, calcium and fibre content relative to current Australian Guide to Healthy Eating (AGHE) and Nutrient Reference Values (NRVs) respectively [23, 24]. Under-reporting of energy intake was assessed using Goldberg thresholds [25]. A ratio of reported energy intake (EI) and basal metabolic rate (BMR) was calculated and compared to the cut-off limits of 0.9. Under-reporters were additionally evaluated against body composition goals.

NK was assessed using the previously validated Nutrition for Sport Knowledge Questionnaire (NSKQ) [26]. The demographic section of the NSKQ asks about athlete characteristics (i.e. age and playing experience). The NSKQ contains 89 knowledge questions that assess various principles of sports nutrition. It is divided into six NK sub-sections; Weight Management (n = 13), Macronutrients (n = 30), Micronutrients (n = 13), Sports Nutrition principles (n = 13), Supplements (n = 12) and Alcohol (n = 8). Knowledge scores were calculated according to the NSKQ protocol [26]. Athletes were instructed to select agree/disagree or choose the correct answer from 4 to 5 possible options; they were told to select ‘not sure’ when appropriate. Overall performance in the NSKQ was assessed using the scoring system of; “poor” knowledge (0–49%), “average” knowledge (50–65%), “good” knowledge (66–75%) and “excellent” knowledge (75–100%) [26]. Participants were asked to complete the questionnaire using their own knowledge without the use of other resources (i.e. internet, peers etc.). All questionnaires were completed within a specified period of 7 days, consistent among individuals, in conjunction with the collection of dietary intake (i.e. 7-day food diary) and body composition data. Responses to an additional 19 investigator-designed questions regarding the participants’ views on current dietetic support, preferred means of education, self-efficacy of cooking skills and nutrition habits (i.e. portion sizes and periodization) were also collected. Responses to these additional questions were not included as a part of the overall knowledge score.

Statistical analysis was conducted using IBM SPSS Statistics for Windows Version 24.0 (IBM Corp, Armonk, New York, USA, 2013). Statistical significance was set at p < 0.05. Shapiro-Wilk test for normality was used to assess if the data was normally distributed. Data was presented as means, standard deviations (±) and percentage of energy and macronutrient requirements (%) met, unless otherwise specified. Independent and paired sample t tests and ANOVA were used to assess significance between different groups (i.e. age, playing experience, education status). The proportion of participants meeting dietary intake recommendations for energy, carbohydrate, protein, fat, fibre, calcium and food groups (i.e. fruit and vegetable) was reported (Table 2). Dietary intake was then analysed in tertiles (i.e. low, medium, high intakes) against NK, age (years), playing experience (years) and education status, using a Chi-square test for independence.

Pearson’s correlation coefficient was used to describe the correlation between NK (i.e. overall score and subsection scores) and diet quality (i.e. % met total energy, macronutrients, fibre, calcium and food groups). They were interpreted according to correlation size (small: r = 0.10–0.29, moderate: r = 0.30–0.49 and large: r = 0.50–1.0) [27, 28].

None of the participants met current recommendations for energy and carbohydrate intake and only 54% met protein recommendations (Table 2). The majority also failed to meet calcium and fibre recommendations (Table 2). The majority (78%) of participants’ EI:BMR ratios were calculated as being below the Goldberg cut-off limit of 0.9 for a plausible intake. Participants who were not classified as under-reporters had body composition goals that aimed to increase total mass. Periodization of macronutrients such as carbohydrate was found to be inconsistent with recommendations, with food diaries demonstrating that the highest carbohydrate intake was found on light training days, followed by main training and recovery days respectively. On main training days, refined carbohydrate sources (i.e. processed and high GI carbohydrate foods) made up 60% ± 20% of total carbohydrate intake.

Relationships between meeting and not meeting recommendations for intake of total energy, carbohydrate, protein, fat, fibre, calcium and food groups (i.e. fruit and vegetable) and education status, level of playing experience and age were explored. Significant positive associations were found between education status and energy intake (P = 0.037) and vegetable intake (P = 0.028). There were no significant relationships found between education status and carbohydrate, protein, fat, fibre, calcium and fruit intake. No significant relationships were found between measures of dietary intake and age or level of playing experience.

The response rate for NSKQ was 100%. The mean NK score for participants was 41 ± 13 out of a possible 89 points (46% mean score). The data was normally distributed. While overall NK was poor, participants performed best in macronutrient, weight management and sports nutrition sections (Table 3). A detailed description of professional and recreational AF players’ NK, assessed using the NSKQ are reported elsewhere [29].

Participants reported a high level of confidence in performing food related behaviours such as cooking a healthy meal (86.0 ± 1.2%). All participants responded that they sought nutrition advice and information from a dietitian (100%, n = 46) followed by teammates (80%, n = 37) and coach or trainer (41%, n = 19). There was a moderate positive association between NK scores and meeting estimated energy requirements (r = 0.325, P = 0.031). NK scores were also positively associated with protein (r = 0.348, P = 0.021), fibre (r = 0.510, P = 0.001) and calcium intakes (r = 0.428, P = 0.004).

The aim of this study was to assess the dietary intake of professional AFL athletes during a pre-season training week where body composition assessments were undertaken. The main findings of the current study were (1) none of the AFL athletes met recommendations for energy or carbohydrate, (2) there was a moderate, positive association between NK and meeting energy requirements, (3) on average athletes had poor nutrition knowledge and (4) higher levels of education were associated with higher intakes of energy and vegetables. This supported our hypothesis that athletes would have inadequate dietary intakes surrounding body composition assessment.

When data was explored in tertiles (i.e. low, medium and high) of energy intake, we found that athletes who had a greater energy intake reported a higher education status. Research by Johansson et al. explored under and over reporting of dietary intake in a non-athlete population and found a positive relationship between energy intake and education status [30]. Collectively, this suggests that education status may positively influence energy intake. Nonetheless, while education status improved energy intake in this cohort, 100% of athletes failed to meet energy intake recommendations [4, 20, 21]. Future research may explore the efficacy of different energy intake education approaches and other factors pertaining to energy intake in these athletes. However, although 78% of participants’ EI:BMR were calculated as being below the Goldberg cut-off of limit of 0.9 for a plausible intake, it is recognised that a lower than expected EI:BMR value may not simply be “under-reporting” but may instead indicate dieting or unusually low consumption during this training week. In this study, reports from participants suggest that whilst they did not meet energy and macronutrient recommendations, the food diaries are likely to be representative of actual dietary intake during that time period and low intakes are not due to under-reporting. Athletes are faced with external influences that may challenge diet adequacy and recovery. Body composition goals and pressures for athletes to make weight are a widely researched topic in professional sport [31, 32]. In particular, research has suggested that when body composition goals are present (i.e. required to lose fat mass or maintain a lean physique) there is usually an energy deficit and macronutrients required for refuelling and recovery are reduced [15]. In this study, food diaries were recorded during a time when the body composition of the AFL athletes was being assessed via DEXA scan. It is possible there was an intentional restriction of energy and carbohydrate intake as a result of aiming to meet the target body composition goals set for athletes, which was reflected in food records. This is supported by research that reported a desire for weight reduction as an explanation for failing to meet energy intake guidelines [30]. Taken together, our findings and those of others may indicate that psychological factors not quantified in this study may contribute to energy intake. To prevent training adaptions and recovery being compromised, it is important to promote realistic body composition goals to athletes. Future studies may use qualitative methods to explore the reasons for athletes not meeting energy and macronutrient recommendations in the time prior to a body composition assessment, and to highlight periods where under-fuelling may be present. This information may also provide a greater insight into the scheduling of body composition assessments in professional AFL programs. To promote a diet that supports performance and decrease the risk of injury, DXA scans may need to be avoided around key training sessions and game days.

Fluctuations in dietary intake that occur over a training week can be a result of several factors including hormonal influences on appetite regulation and time constraints within training schedules. Self-reported energy intake showed a level of periodization, with the largest energy intake reported on main training days followed by light and recovery days respectively. Similarly, participants had a good level of understanding of sport nutrition principles regarding periodization of carbohydrates and energy availability. Indeed, most participants (59%) reported that ‘periodizing energy and carbohydrate intake (i.e. high versus low days)’ was good practice for body composition management and performance [33]. Despite good knowledge of energy and carbohydrate periodization principles, total intake did not meet recommendations. Maintaining a diet that is energy and macronutrient deficient can increase the risk of illness and injury, promote weight loss (including losses to lean muscle mass) and alter performance outcomes [4]. Reasons why athletes may not consume adequate nutrients and energy during weeks of high training loads have previously been explored [8, 9]. In particular, physiological factors such as hormones may affect an athlete’s ability to consume the right amount of nutrients and energy required on main training days. Research has explored the influence of high intensity training on changes to appetite hormones and found that hormones such as ghrelin, glucagon-like peptide-1 (GLP-1), pancreatic polypeptide (PP) and peptide YY (PYY) may contribute to suppressed hunger post physical activity [9]. Furthermore, logistical issues such as time constraints on main training days may have additionally limited participants’ ability to consume adequate nutrition on main training days. Indeed, athletes appeared to choose convenient, energy dense snacks and refined carbohydrate sources on main training days. The potential provision of meals in this professional setting may positively support dietary intake over a training week. Future research may investigate whether the provision of meals in professional settings with the inclusion of flexible meal times in training schedules to accommodate those that experience supressed appetite post training can have a positive influence on athletes’ dietary intake over a training week.

This study demonstrated that on average, AFL athletes’ dietary carbohydrate intake was relatively low in comparison to requirements based on training loads [3, 6]. This corresponds with previous research that reported dietary intakes of Australian footballers to fall below carbohydrate recommendations [3, 6]. Mean dietary carbohydrate intakes (2.4 ± 0.7 g·kg^{− 1}·day^{− 1}) fell below the lower range of sports nutrition guidelines advocated by the IOC (3–12 g·kg^{− 1}·day^{− 1}) [4, 34]. Carbohydrate is a key fuel used for high-intensity aerobic based sports like Australian football and plays a vital role as a muscle substrate for performance and recovery [35]. Low carbohydrate availability can lead to muscle glycogen depletion and subsequent fatigue, impaired concentration and decreased physical output [34, 36].

Research has suggested that carbohydrate intake is often the main macronutrient restricted by individuals to meet body composition goals [15]. Athletes had overall sound knowledge in the NSKQ macronutrients subsection, however it was found that knowledge regarding carbohydrate recommendations was poor. Therefore, poor NK may contribute to athletes having under-consumed carbohydrate. A large proportion of participants (41%) were ‘Not Sure’ regarding carbohydrate recommendations for athletes undertaking moderate to high intensity physical activity and only 24% of participants correctly identified carbohydrate recommendations. The present results are consistent with previous research, which found that 53% of collegiate athletes, when asked to identify carbohydrate recommendations, selected values that fell below carbohydrate recommendations advocated by sports nutrition experts [37, 38]. Previous findings show athletes have a misconception regarding carbohydrate availability (i.e. carbohydrate intake is related to body fat gains) and taken together the results of this study suggests that there is a knowledge gap regarding the importance of carbohydrate as a main fuel. It is important to promote individualised carbohydrate recommendations according to training requirements to support body composition goals and training adaptions of athletes. Further education that highlights the important role of carbohydrate in fuelling training and competition may be required, in addition to tailored advice to improve carbohydrate intake.

Self-reported protein intakes in the present study were equal to or in excess of current recommendations for football and power sports [39]. Current recommendations suggest an absolute dose of 20–40 g per serve [39]. Indeed, 30% correctly responded to the statement ‘Protein absorption in a single sitting is limited’. Despite this, only 15% of participants reported that their usual serving size aligned with this recommended serve. It has been suggested that when energy availability is low (due to energy restriction aiming for weight loss), protein intake may be increased, to preserve muscle mass [40]. Due to the overall low energy intakes reported by athletes, excess protein ingestion may have been required to meet training demands. Extensive media attention regarding high protein, low carbohydrate diets may have additionally negatively influenced knowledge–creating an uncertainty regarding the distribution of macronutrients in the diet [10]. Future research could examine the scope of effect that media messages promoting low carbohydrate, high protein diets for weight loss, has on individuals’ dietary behaviours. Likewise, providing information regarding an athlete’s individual protein requirement and linking this information with education tools that focus on portion sizes of protein (e.g. serving size plates, visual portion guides) may help guide informed decisions about protein and simultaneously promote carbohydrate intakes that are better aligned with current guidelines.

Athletes were asked to give feedback regarding current dietetic support. Participants ranked individual consults as the most useful means of obtaining nutrition advice, in contrast to group presentations that were ranked as least useful. Given the findings of this study and the suitability of practical group based education in professional sport, these programs may need to include a more individualised approach that includes a greater focus on providing evidence-based information regarding energy and macronutrient requirements. Education that links dietary intake to performance may act as a motivator for behaviour change and limit the impact of other external factors, such as body composition assessments, on intake. Future research may investigate the efficacy of current practical nutrition education strategies such as cooking programs, in order to determine their effectiveness with the professional athlete cohort.

While this study aimed to assess the dietary intake of professional AFL athletes, it possesses several limitations. Firstly, the study sample included only one professional AFL team (n = 46) therefore the findings may reflect the individual characteristics and demographics of the group observed. Moreover, time constraints meant that only cross-sectional data could be collected. In addition to this, weighed food diaries are considered gold standard when assessing dietary intake in athletes; however, due to time and logistical constraints estimated food diaries were deemed the most appropriate method for use in this study.

Under-reporting is a well-documented limitation when using diet-recall methodologies to report nutrient intakes, especially in healthy cohorts [41]. Due to technological issues, 4 of the 46 participants in this study used ‘WhatsApp’ to report their dietary intake, as opposed to the vast majority of participants (n = 41) who used ‘Easy Diet Diary’. Those participants using ‘WhatsApp’ sent photos of their meals to the dietitian, who estimated portions and nutrient intake from this information, possibly resulting in the inaccurate measurement of dietary intake.

All datasets generated during this study are included in this published article.

The La Trobe University Human Research Ethics Committee approved the study (S17–025). All participants were informed about the procedures and signed an informed consent form prior to commencement of the research.

Participants signed consent forms for publication.

The authors declare that they have no competing interests.

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All datasets generated during this study are included in this published article.