Nutrition knowledge and dietary intake of hurlers

Abstract

The current study investigated the association between sports nutrition knowledge and dietary quality in a sample of adult Irish male hurling players. Nutrition knowledge was measured by the validated Sports Nutrition Knowledge Questionnaire (SNKQ). Diet quality was measured by the Australian Recommended Food Score (ARFS) calculated from food frequency questionnaire data. Analysis of variance and linear modelling were used to assess associations between variables. A total of 265 (129 elite, 136 sub-elite) players were recruited. No significant difference in nutrition knowledge (SNKQ) was found between groups. Results showed a significant difference (p = 0.02; d = 0.39 ± 0.25; small) in food score (ARFS) between groups. A weak, positive association (r = 0.3, p = 0.007) was found between nutrition knowledge and food score. Elite level players, aged 28–32, with college degrees, that have previously received nutritional guidance displayed the highest levels of both nutrition knowledge and food score. Higher levels of nutrition knowledge and food score were expected in elite players, however were only found in food score. Nutrition knowledge does contribute to dietary quality although future interventions should focus on specific gaps in knowledge such as how to meet total energy/carbohydrate requirements.

Keywords

Food frequency questionnaire Gaelic sport hurling

Introduction

Hurling is an intermittent type stick and ball invasion game that is native to Ireland.¹ Hurling consists of two teams of fifteen players and belongs to a family of other sports that include gaelic football, camogie, handball, and rounders. It is played in many countries across the world but enjoys the majority of its popularity in Ireland and amongst the Irish diaspora abroad. Hurling is governed by the Gaelic Athletic Association (G.A.A.) and is the third most popular team sport in Ireland, in terms of participation.² The G.A.A. is an amateur organization where all hurling players represent a sub-elite team (club) with the best players from each club selected to represent their local elite (inter-county) team.³ Elite teams compete for the Liam McCarthy Cup during a playing season that typically lasts from February to September.¹ Top-level games are regularly played in front of crowds exceeding eighty thousand people. A game of hurling is contested by two teams of 15 players each (1 goalkeeper and 14 outfield players) on a pitch (140 m × 90 m) which is approximately 40% larger than a typical soccer pitch (110 m × 70 m).⁴ Elite games have a minimum duration of 70 minutes (35 minutes per half) while sub-elite games have a minimum duration of 60 minutes (30 minutes per half).⁴ Hurling’s primary objective is to outscore the opposition by striking the ball through the opposition goalpost. A goal (worth three points) is awarded when the ball crosses the line beneath the crossbar while a point (worth one point) is awarded when the ball crosses the line above the crossbar.⁴ This invasion-type game places high physical demands on players with periods of high-intensity efforts that are common in other sports. Independent of position, average match play demands of elite hurlers are 7,358 ± 1,085, 759 ± 206, and 486 ± 127 for total distance (TD), high-speed running (HSR), and sprint distance (SD), respectively. The maximal relative demands of elite players for TD, HSR and SD were 184 ± 21, 51 ± 13, and 42 ± 10 m·min⁻¹, respectively.⁵ Sub-elite players have been found to perform significantly lower relative TD running speeds than elite players and had greater reductions in running speed during the second half.⁶ Elite G.A.A. players, despite their amateur status, follow a quasi-professional training regimen³ and are often required to attend six collective sessions per week comprised of both pitch-based and gym-based training. Balancing training with work or study and personal lives places many demands on players in terms of travel distances, and time and fatigue management.⁷

Research concerning the gaelic sports (gaelic football and hurling) has grown considerably in recent years since the original investigations examining anthropometrics of individual athletes^8–10 while more recent research, with the growing availability of global positioning systems (GPS) units, has focused on the workload of players during games and practice.^11–13 The nutritional requirements of G.A.A. players has also begun to develop with most attention focused on gaelic football,^14–16 although some work has also been done in hurling.¹³ The positive impacts of well-designed nutrition interventions have been shown to enhance the performance of top level athletes through improvements in body composition^17,18 increasing total energy intake¹⁹ and meeting macronutrient recommendations for a particular sport.²⁰ The increase in internationally endorsed dietary guidelines for athletes has been reflected by the publication of several consensus statements on best practices around food timing and intake, fluid and sodium balance, and the use of supplements.^21,22 Despite these guidelines, research indicates that most athletes, particularly at the sub-elite level, have sub-optimal dietary intakes.^23,24

A number of examples have been found of dietitians centring their dietary interventions on nutrition education to improve awareness of and compliance with nutritional recommendations for a particular sport.²⁰ A number of these education programs have not been evaluated, while many of the evaluations have focused purely on nutrition knowledge and not on their influence on dietary intake or athletic performance. International studies of professional and semi-professional athletes,²⁵ and elite gaelic footballers,^15,16,26 have found the majority do not meet the sport nutrition recommendations, particularly in terms of carbohydrate ingestion.^27,28 While no specific investigation has been conducted on the nutrition practices of hurlers, the reductions in high speed running distance and sprint distance, by 10% and 8% respectively, during match play⁵ suggest that players are not adequately fuelled for the demands of the game.

Nutrition knowledge is one of a few modifiable, and easily quantified, determinants of dietary behaviours¹⁹ although a multitude of other factors do contribute to nutritional intake. Previous investigations have found that greater nutrition knowledge in athletes was associated with higher carbohydrate, vegetable and fruit intakes.²⁹ Previous investigations have found inadequate levels of nutrition knowledge in gaelic footballers^26,30 while no such investigation has yet been conducted in hurlers. A baseline of knowledge is key to developing potential interventions in the future while also assessing the role knowledge has on quality of diet, although as nutrition knowledge is a dynamic process, the use of validated and reliable tools to measure the current base is required.³¹ Examining associations between knowledge and diet is challenging as dietary intake is difficult to quantify²³ particularly in athletes who generally have much higher energy and macronutrient demands. Dietary intake has recently been assessed using food frequency questionnaires, such as the Australian Recommended Food Score (ARFS), in both general population samples^32,33 and in an athletic population.²³

The current evidence base is lacking a definitive starting point from which a body of research regarding nutrition practices in hurlers can begin. The current study aims to find a baseline of nutrition knowledge in hurling players while also examining the association between nutrition knowledge and dietary intake. The current study also aims to examine the association and most influential demographic factors that link nutrition knowledge to quality of dietary intake. The authors hypothesise that there is a positive association between nutrition knowledge and dietary and that elite players have significantly greater levels of nutrition knowledge and dietary intake.

Methods

Participants

Participants were a sample of elite male adult hurling players from 6 counties in Ireland. Thirteen counties play hurling at the elite level (Liam McCarthy Cup 2017 season) and each team carries a panel of 30–36 players. A statistical power analysis was performed using Cohen’s (1988) criteria for a medium effect (d = 0.3) with an alpha of .05 and found n = 129/136 would provide power = 0.79 for the simplest between group comparison. The 6 counties that participated were from a variety of geographical locations throughout the island of Ireland (Munster = 2, Leinster = 3, Connacht = 1). The sub-elite participants were from the same counties as the elite players but participated in lower level club competitions (junior, intermediate or senior). Club teams generally carry a smaller number of players, so it was necessary to survey 2 or 3 different clubs from each county to meet the required number of participants. The study was approved by the Ethics Committee of the University of Chester, 1350/17/JM/CSN. Participation was anonymous, and all participants were provided with participant information sheets. All participants were over the age of 18.

Study procedure

The 2 questionnaires were distributed to participants at the same time and filled in with pen and paper or using an online form on mobile phone or tablet. The means of completing the questionnaire was at the discretion of team management. Each team was visited on 1 occasion when all questionnaires had to be filled in under supervision from the primary investigator. Questionnaire completion time ranged from 12–18 minutes. Permission had previously been sought from team management who were aware of the study in advance of ethical approval. The primary investigator could clarify any queries regarding the study design or specific meanings of questions as the athletes filled out the questionnaires. Participants were made aware that they could not withdraw their questionnaire once it had been submitted as all were anonymous and it would be impossible to tell each one apart when compiled together.

Dietary assessment

Quality of diet was assessed using a validated Food Frequency Questionnaire (FFQ), the Australian Recommended Food Score (ARFS).²¹ The ARFS was self-administered immediately prior to the Sports Nutrition Knowledge Questionnaire. Participants indicate (by checking a box) how often (times per day, week or month) and how many standard servings each of the approximately 200 foods they usually consume in the semi-quantitative FFQ. The ARFS was previously validated in an adult population³² and demonstrated an intraclass correlation coefficient of 0.87. It has also been previously used in measuring quality of diet in athletic populations.²³

Demographic variation

The middle section of the questionnaire presented demographic questions including age, highest level of education, playing level and access to nutritional guidance.

Sports nutrition knowledge questionnaire

The validated Sport Nutrition Knowledge Questionnaire was used to measure nutrition knowledge. The SNKQ was derived from the widely used General Nutrition Knowledge Questionnaire (GNKQ)³⁴ and was validated in groups of both athletes and dietetic students.³⁵ The questionnaire had previously been used to measure nutrition knowledge in both coaches³⁶ and athletes.^30,37,38 The questionnaire comprised 88 sports nutrition knowledge questions, and was divided into five main knowledge sub-categories; Nutrient types (46 questions), which comprised questions pertaining to macronutrients and micronutrients, recovery (7 questions), fluid (9 questions), weight management (15 questions) and supplements (11 questions). Minor adjustments were made to 4 questions as the foods/drinks in question were not readily available in Ireland. Consultation with the original questionnaire designer was sought during the adjustment process. Each question in the questionnaire could be answered “yes, “no” or “unsure”. Scores were coded as +1 for a correct response and 0 for an incorrect response. “Unsure” responses were added as an option to discourage participants from guessing answers.

Statistical analysis

Analyses were conducted in R (R Core Team, 2014) and figures were produced using the package ggplot2 (Wickham, 2009). When fitting a General Linear Model to the data, assumptions were checked by examining the residuals versus fitted values plot and the normal probability plot of the standardised residuals. Descriptive statistics were tabulated for participants demographic data, total score and sub-category mean scores in both Sports Nutrition Knowledge and Dietary Quality. Independent t-tests, using a significance level of p < 0.05, were used to show mean correct, incorrect and unsure score differences between the two groups of athletes (elite and sub-elite) when normally distributed while Mann-Whitney tests were used where not normally distributed. One-way analysis of variance was used to determine the associations between total nutrition knowledge scores for all participants and the 4 variables; age, education, level of participation, and access to a nutritionist. Cohen’s D was used for calculating and reporting effect sizes. Confidence intervals were set at 95%. When variables were not normally distributed, Spearman’s Rank Correlation coefficient was used instead of Pearson’s Product Moment to determine the association between nutrition knowledge and quality of diet.

Results

Participant characteristics

265 (136 sub-elite and 129 elite players) participants responded to the questionnaire. Normality tests (Kolmogorov-Smirnov) and plots found that Food Score was not normally distributed so descriptive statistics will be reported through median values. Non-parametric tests will also be used to analyse differences and relationships that involve food score. Demographic characteristics of participants (n = 265, 136 sub-elite and 129 elite) are summarised in Table 3.

Elite players

32 players fit in the 18–21 age bracket, 78 in the 22–27 age bracket, 18 in the 28–32 age bracket, and 1 in the 33+ age bracket. 32 players had post-graduate degrees which was the highest recorded level of education. The lowest level of education was Leaving Certificate (equivalent to A-Levels) with 35 playrers. 4 have completed Post-Leaving Certificate courses, 7 have achieved Diplomas, 42 have achieved Degrees and 9 have achieved trades through the FAS apprenticeship scheme.

Sub-elite players

38 participants fit in the 18–21 age bracket, 48 in the 22–27 age bracket, 29 in the 28–32 age bracket, and 21 in the 33+ age bracket. 22 players had post-graduate degrees which was the highest recorded level of education. The lowest level of education recorded was Junior Certificate (equivalent to GCSE) with 10 players. 30 have completed Leaving Certificate (equivalent to A-Levels), 7 have completed Post-Leaving Certificate courses, 6 have achieved Diplomas and 47 have achieved Degrees. 14 players have achieved trades through the FAS apprenticeship scheme.

Nutrition knowledge

On average, participants scored 48.8% in the Sports Nutrition Knowledge Questionnaire and are summarised in Tables 1 and 4. Scores for Section 2 (Fluid; 62.5%) were highest, and were lowest for Section 6 (Supplements; 27.3%). Median nutrition knowledge scores were higher for elite players (43 (IQR = 38-47) out of 86 (50%)) than sub-elite players (42 (IQR = 36-48) out of 86 (48.8%)) however the difference was not significant (p = 0.56). Elite players (50%) scored slightly higher than sub-elite players (48.8%) in Section 1 (Nutrients) and significantly higher (p = 0.002; d = 0.4 ± 0.27; small) in Section 2 (Fluid). No differences were observed for any of the other sections. ANOVA test and Tukey post-hoc analysis found a significant difference in nutrition knowledge between players in the 18–21 age bracket (47.7%) and the 28–32 age bracket (51.2%) (p = 0.001; d = 0.32 ± 0.19; small), and between players in the 33+ age bracket (47.7%) and the 28–32 age bracket (51.2%) (p = 0.007; d = 0.39 ± 0.21; small). ANOVA test and Tukey post-hoc analysis found a significant difference in nutrition knowledge between Junior Cert (39.5%) & Degree (50%) (p = 0.001; d = 0.23 ± 0.15; small), Junior Cert (39.5%) & Post-Graduate Degree (50%) (p = 0.003; d = 0.21 ± 0.13; small), PLC Course (37.2%) & Degree (50%) (p = 0.006; d = 0.31 ± 0.19; small), and PLC Course (37.2%) & Post-Graduate Degree (50%) (p = 0.004; d = 0.28 ± 0.17; small). ANOVA test and Tukey post-hoc analysis found a significant difference in nutrition knowledge between players who received nutritional guidance from a club coach (45.3%) and those who have access to a full/part-time nutritionist (51.2%) (p = 0.002; d = 0.39 ± 0.22; small).

Table 1.

Median scores on the sports nutrition knowledge questionnaire.

	Elite players (n = 129)		Sub-elite players (n = 136)		Combined (n = 265)
	Median & %	IQR	Median & %	IQR	Median & %	IQR
Section 1	22 50%	19–24	21.5 48.8%	18–24	22 50%	18–14
Section 2	5^a 62.5%	4–6	4^a 50%	3–5	5 62.5%	4–6
Section 3	5 45.5%	4–7	5 45.5%	4–7	5 45.5%	4–7
Section 4	1 25%	1–2	1 25%	1–2	1 25%	1–2
Section 5	7 58.3%	6–8	7 58.3%	5–8	7 58.3%	6–8
Section 6	3 27.3%	2–4	3 27.3%	2–4	3 27.3%	2–4
Total	43 50%	38–47	42 48.8%	36–48	42 48.8%	37–47

IQR: inter quartile range.

^aSignificant difference (p < 0.005) in fluid knowledge between elite & sub-elite groups.

Table 2.

Median scores on the Australian Recommended Food Score.

	Elite players (n = 129)		Sub-elite players (n = 136)			Combined (n = 265)
	Median & %	IQR	Median & %		IQR	Median & %	IQR
Vegetables	10 47.6%	8–13	10 47.6%	8–12		10 47.6%	8–12
Fruit	5^a 45.5%	4–6	4^a 36.4%	3–6		5 45.5%	4–6
Meat	4 57.1%	3–5	4 57.1%	3–5		4 57.1%	3–5
Meat alternative	3 50%	3–4	3 50%	2–4		3 50%	3–4
Grains	6 46.2%	5–7	6 46.2%	5–7		6 46.2%	5–7
Dairy	5 45.5%	4–6	5 45.5%	4–5		5 45.5%	4–6
Water	1 100%	1–1	1 100%	1–1		1 100%	1–1
Extras	1 100%	0–1	1 100%	0–1		1 100%	0–1
Total	35^b 48.6%	31–40	33^b 45.8%	29–38		35 48.6%	30–39

IQR: inter quartile range.

^aSignificant difference (p < 0.005) in fruit score between elite and sub-elite players.

^bSignificant difference (p < 0.01) in total food score between elite and sub-elite players.

Table 3.

Sub-group nutrition knowledge and food score.

		Nutrition knowledge		Food score
Variable	N	Median & %	IQR	Median & %	IQR
Age
18–21	70	41^a 47.7%	38–46	32^b 44.4%	26–37
22–27	127	43 50%	36–49	35^b 48.6%	31–39
28–32	47	44^a 51.2%	37–47	35^b 48.6%	32–40
33+	21	41^a 47.7%	37–44	35 48.6%	32–37
Education
Junior Certificate	10	34^c 39.5%	27–43	33 45.8%	29–33
Leaving Certificate	65	42 48.8%	39–47	33 45.8%	26–38
PLC	9	32^c 37.2%	28–45	33 45.8%	29–43
Diploma	13	41 47.7%	39–46	35 48.6%	32–36
Degree	91	43^c 50%	38–50	37^d 51.4%	33–40
Post–Graduate	54	43^c 50%	35–47	33 45.8%	30–37
Trade	23	44 51.2%	40–49	32^d 44.4%	31–38

IQR: inter quartile range.

PLC: Post-Leaving Certificate.

^aSignificant difference (p < 0.005) in nutrition knowledge between players in the 18-21 age bracket & the 28-32 age bracket and between players in the 33+ age bracket and the 28-32 age bracket.

^bSignificant difference (p < 0.005) in food score between players in the 18-21 age bracket & the 22-27 age bracket and between players in the 18-21 age bracket & the 28-32 age bracket.

^cSignificant difference (p < 0.005) in nutrition knowledge between Junior Cert & Degree, Junior Cert & Post-Graduate Degree, PLC Course & Degree, and PLC Course & Post-Graduate Degree.

^dSignificant difference (p < 0.005) in food score between players who have a degree and those who have a trade.

Table 4.

Level of nutritional guidance, nutrition knowledge and food score.

		Nutrition knowledge		Food score
Nutritional guidance	N	Median & %	IQR	Median & %	IQR
Club coach	20	39^a 45.3%	35–42	35 48.6%	33–38
Full/part time nutritionist	129	44^a 51.2%	38–48	35^b 48.6%	31–40
Nutrition talks with club	53	42 48.8%	37–47	35 48.6%	32–39
Nutrition talks with academy	33	41.5 48.3%	38–49	33 45.8%	29–39
No formal guidance	30	40 46.5%	35–46	30.5^b 42.4%	28–36

IQR: inter quartile range.

^aSignificant difference (p < 0.005) in nutrition knowledge between club coach and full/part time guidance from a nutritionist.

^bSignificant difference (p < 0.005) in food score between full/part-time guidance from a Nutritionist & those with no formal nutritional guidance.

Food score

Overall Australian Recommended Food Score was 35 (IQR = 30-39) out of a possible 72 (48.6%). Highest scoring domains were water (100%) and other (100%) while fruit (45.5%) and dairy (45.5%) were the lowest. Median food scores were significantly higher (p = 0.02; d = 0.39 ± 0.25; small) for elite players (Mdn of 35 (IQR = 31-40) out of 72 (48.6%)) than sub-elite players (Mdn of 33 (IQR = 29-38) out of 72 (45.8%)) as can be seen in Figure 1 and Table 2. Kruskal-Wallis tests found a significant difference (p = 0.007; d = 0.32 ± 0.19; small) in fruit score between elite (45.5%) and sub-elite players (36.4%) as can be seen in Table 2.

Figure 1.

Significant difference (p = 0.02) in food score between elite & sub-elite hurling players.

Kruskal-Wallis tests found a significant difference in food score between players in the 18–21 age bracket (44.4%) and the 22–27 age bracket (48.6%) (p = 0.006; d = 0.21 ± 0.13; small), and between players in the 18–21 age bracket (44.4%) and the 28–32 age bracket (48.6%) (p = 0.004; d = 0.2 ± 0.09; small). Kruskal-Wallis tests found a significant difference in food score between players that have achieved a degree (51.4%) and players that have achieved a trade (44.4%) (p = 0.008; d = 0.23 ± 0.14; small). Kruskal-Wallis tests found a significant difference in food score between players who have access to a full/part-time nutritionist (48.6%) and those who received no formal guidance (42.4%) p = 0.005; d = 0.22 ± 0.09; small).

Association between nutrition knowledge and food score

Spearmans correlation found a weak to moderate positive association (r = 0.3, p = 0.007) between nutrition knowledge and food score in the entire sample of hurling players as can be seen in Figure 2. Spearmans correlation found a weak positive association (r = 0.26, p = 0.002) between nutrition knowledge and food score in sub-elite hurling players. Spearmans correlation found a moderate positive association (r = 0.35, p = 0.006) between nutrition knowledge and food score in elite hurling players as can be seen in Figure 3.

Figure 2.

Relationship between Nutrition Knowledge and Food Score in entire sample of hurling player.

Figure 3.

Relationship between Nutrition Knowledge and Food Score in elite hurling players (r = 0.35) and sub-elite hurling players (r = 0.26).

Discussion

The current study is one of only a few investigating the association between nutrition knowledge and diet-quality in athletes, and the first of its kind in hurlers. This provides researchers and practitioners alike with a starting point from which to base future interventions and guidelines. Nine previous studies have examined this association in athletic populations with only three in the recent past.^23,30,39 All other investigations were carried out before 1996 with sample sizes ranging from 14 to 122, five of which had less than 100 athletes which makes the current sample of 265 one of the largest and most comprehensive to date. Overall SNKQ score was 48.8% with no significant difference between elite- and sub-elite players. Overall ARFS score was 48.6% with a significant difference found between elite and sub-elite players. There was no significant effect of age, playing level or nutritional guidance on nutrition knowledge although level of education did have an effect on variance at 6.7%. No significant difference was found between elite and sub-elite players in total nutrition knowledge although elite players did score significantly higher than sub-elite in the section on fluids. This did not, however, translate to any difference in terms of water or fluid intake between groups in the food score questionnaire. A weak significant association was observed between higher nutrition knowledge, which explained 8.7% of the variance in overall diet quality. Nutrition knowledge had the largest impact on variance while level of education had the second largest at 6.9%. Playing level had no impact on nutrition knowledge at 0.1% as was also highlighted with no significant difference found between elite and sub-elite players in terms of nutrition knowledge. Playing level did account for variance in food score at 2.8% which was also highlighted in the significant difference found between elite and sub-elite players.

Nutrition knowledge was less than a previous sample of mixed Irish athletes who scored 53% on the same measurement tool.³⁰ The current reported results are also substantially lower than previous samples in both British⁴⁰ and Australian^41,42 community samples. Previous studies using the same tool to assess nutrition knowledge have shown mixed results ranging from 33% in Iranian athletes³⁸ to 49% in American athletes³⁷ and 53% in Irish athletes.³⁰ Percent dietary quality was higher than all previously sampled community groups but was lower than a sample of Australian athletes,²³ although only a few studies have been conducted using this particular assessment tool³² so care must be taken when making comparisons. The previous study on an athletic population found a significant difference in food score between females (54.2%) and males (49.4%) which appears to show that quality of diet in both Irish (48.6%) and Australian (49.4%) athletic males is quite similar. As a significant difference was found between elite and sub-elite players in terms of food score, the main difference appears to have been caused by intake of fruit with elite players scoring significantly more than sub-elite. The fruit intake in both groups was, however, considerably lower than that of the previously sampled Australian athletic population²³ while meat, meat alternatives and water were all considerably higher. This would suggest Irish hurling players are more likely to meet current daily protein requirements of 1.6 to 2.2 g/kg/day^43–45 from their larger intake of meat and meat alternatives which would be consistent with previous findings in gaelic footballers who consumed 2.1 g/kg/day.¹⁶ This suggests the messaging from practitioners in terms of increased protein intake for athletes has had the desired effect. High scores for water are consistent with sports nutrition guidelines recommending adequate hydration to promote safe and optimal sports performance⁴⁶ which also suggests athletes are adhering to good hydration practices.

Intake of grains in both elite and sub-elite groups was considerably lower than the previous Australian group which shows that many athletes may not be meeting current sports nutrition recommendations which promote adequate intake of carbohydrate foods to enhance sports performance.⁴⁷ A sample of Irish gaelic football players, who are a very similar population demographic to Irish hurling players, were found to be consuming well below the recommended amounts for total calories and carbohydrates during a pre-season period.¹⁶ A similar study found Australian football players to be consuming well below the recommended amounts for total calories and carbohydrates in a pre-season week but did note that players with higher levels of nutrition knowledge did consume greater total calories, predominantly from extra carbohydrates.⁴⁸ This suggests increasing nutrition knowledge in athletes may lead to them meeting total energy and carbohydrate recommendations which would support increased performance in both training and competition periods as has been previously demonstrated in elite soccer players.⁴⁹

Consistent with a number of studies in the existing literature^23,50,51 the association between nutrition knowledge and food score was significant and positive but weak, although slightly stronger in elite players. No sub-sections of either the SNKQ or the ARFS showed significant associations with increased knowledge or food score which is in contrast to previous studies which have shown positive significant associations between increased knowledge and vegetable intake.^40,50,51 The work of Jenner et al.,⁴⁹ has highlighted the impact increased nutrition knowledge may have on quality of diet, particularly in terms of meeting overall energy requirements and carbohydrate needs. However weak the association, it does appear to be the most likely identifiable factor that will lead to increased dietary quality.

The above results suggest that an elite player, in the 28–32 age bracket, with a degree, that has received nutritional guidance from either a full- or part-time nutritionist, has both the highest level of nutrition knowledge and the highest food score. As the association between nutrition knowledge and food score is weak, although stronger in elite players than sub-elite, it appears that there are many contributing factors to quality of diet other than just nutrition knowledge. Players with access to a nutritionist scored considerably higher than all other sub-groups in terms of nutrition knowledge yet no difference in food score was found between them and those who received guidance from their club coach. This suggests education programmes employed by nutritionists are effective in improving nutrition knowledge, as was also found in previous studies,^17,19 but may not be as effective in improving quality of diet. This may also be due to nutritional guidance often only being applied when athletes are experiencing difficulties with their diet²³ so higher results in food score may not yet be apparent as athletes are still undergoing behaviour change. It is also interesting to note that nutrition knowledge in players who only received guidance from their club coaches was significantly lower than those with access to a nutritionist. This is in line with previous studies who also found that nutrition knowledge in both athletics and rugby coaches was below an accepted standard to be offering meaningful guidance.^52,53 Athletes aged between 28 and 32 were found to have significantly higher levels of nutrition knowledge than athletes aged below 21 and above 33. This may be due to the increasing popularity of nutrition education, through both nutritionists and public health messaging, on athletes that typically reach elite status between 22 and 30 years of age.^42,43 Athletes aged between 18 and 21 years were found to have significantly lower food scores than older athletes. This may be due to the difficulties associated with both acquiring and cooking nutritious foods due to low budgets, poor cooking facilities and lack of access to transportation. It may be worth implementing nutrition education strategies with a practical focus on food preparation for younger athletes so as to increase the likelihood of increased dietary quality as opposed to nutrition knowledge alone. Athletes with the highest qualification of a trade (apprenticeship) had the lowest food score and were significantly lower than those with a degree. Athletes working in trades often do more physically demanding work for longer hours and so depend on convenience foods. This generally leads to lower variety of foods and less intake of fruit and vegetables although further investigation of dietary intake is required in this particular population. Another interesting observation in regard to nutritional guidance was that 50 participants have received no guidance from qualified professionals (20 received guidance from club coach, 30 no formal guidance). This suggests the GAA as an organisation may need to develop and implement a nutrition education program for players at all levels, particularly in older and younger age categories, instead of waiting for players to reach the elite level and possibly be highlighted as having nutritional difficulties.

A major strength of this study was the use of validated instruments to measure both nutrition knowledge and food score in a well-defined population of elite and sub-elite athletes as well as the recruitment of a large sample size.

Limitations

The ARFS was previously validated on an Australian population so may be biased toward the availability and popularity of foods more prevalent in Australia than Ireland. This may explain some of the variance in lower fruit and vegetable scores from the current study although it is unlikely to account fully for the large difference in fruit consumption.

Conclusion

This study demonstrates a positive weak association between nutrition knowledge and dietary quality in adult male hurling players. Only a few studies have examined this association and the existing literature is quite dated. Hurling players, similar to their gaelic football counterparts, appear to be consuming well below the recommended intake of total calories and carbohydrate in particular. Higher levels of nutrition knowledge have been associated with increased total energy and carbohydrate consumption which has proven benefits for performance. This study demonstrates that a substantial proportion of what were predominantly young Irish athletes failed to meet basic dietary recommendations, especially fruit and vegetable intake. Consumption of fluids and dairy were higher in both groups than previously measured samples which highlights a potential strength in the diet of a sample of young Irish athletes.

Recommendations

Nutrition education strategies should be implemented at all levels of competition in hurling players with a particular emphasis on the youngest and oldest cohorts. Strategies to improve both nutrition knowledge and dietary quality should include practical components that focus on meal preparation such as food shopping and cooking skills. Practitioners should develop tailored interventions to increase the chances of overcoming specific barriers and behaviours for specific athletes and sub-groups within teams.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

John Murphy

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