Kinanthropometry and dietary habits of non-professional rugby players

A battery of anthropometric variables (body mass, height, sitting height, bone breadths, limb and trunk girths, and skinfolds) was collected on 57 rugby union players from a Group III Club of the Unión de Rugby de Buenos Aires (URBA) set in a low-income neighborhood. Measurements were performed by level 2 and 3 anthropometrists who adhered to the International Society for the Advancement of Kinanthropometry (ISAK) protocol (15).

Anthropometric tools included an Aspen EB6571 digital strain-gauge scale (Jinli Electronic, Zhongshan, China), wall-mounted portable stadiometers with headboards (Rosscraft SRL, Argentina), a 50-cm sturdy wooden box for sitting height, bone breadth calipers (Rosscraft SRL, Argentina), measuring tapes (Rosscraft, Canada), and Holtain skinfold calipers (Crymych, UK). Body composition was assessed using the Five-Way Fractionation Method (16) which divided the body into anatomically defined tissue masses: adipose, muscle, residual, bone, and skin. Fat mass was estimated using the Yuhasz percent fat equation (17). Body mass index (BMI), weight/height², sum of six skinfolds (S6skf) as the sum in millimeters of triceps, subscapular, supra-spinale, abdominal, front thigh and medial calf skinfolds, and muscle-to-bone ratio (MBR) as muscle mass/skeletal mass (8) were calculated. Registered dietitians interviewed the players to record a 24-hour dietary recall of the previous day's (a Friday) food and beverage intake, using memory cues and strategies to optimize recall. Diet analysis (energy, macro-, micro-nutrients, meal, and food-type energy distribution) was carried out using the SARA nutrition software (version 1.2.12, Ministerio de Salud, Argentina; http://www.msal.gov.ar/htm/Site/ennys/site/sara.asp↗). Micro-nutrient adequacy was established by comparing the reported intakes with the Daily Reference Intakes (DRI) for adult males suggested by the Food and Nutrition Board, Institute of Medicine, USA (http://www.nap.edu/catalog/dri/↗). Additional information on weekly training and daily activities was collected to estimate a physical activity level (PAL) using the Factorial Method (Energy and protein requirements. Report of a joint FAO/WHO/UNU Expert Consultation., 1985). Total energy expenditure estimation (TEE) was calculated by multiplying the PAL by the estimated basal metabolic rate (BMR) using the Schofield equation (18). Validity of reported food intakes was assessed using the suggestions of Black (19), comparing reported energy intake (EI) against calculated TEE. To avoid over-estimating BMR in over-fat players, the Hamwi formula was used to adjust the body weight used in calculating BMR (20). Before testing, the purpose of the study was explained to the players, who signed an informed consent form. Approval for the study was obtained from the Ethics Board at the Departamento de Medicina at River Plate Club.

The SPSS software (v27.0.0.0, SPSS Inc., Chicago, USA) was used for all statistical analyses. Data were presented as the mean ± standard deviation (SD). Assumptions of normality were verified using the Shapiro-Wilk test and student's independent t-test and Mann-Whitney U-test were used for comparison analyses. For all analyses, the criterion for significance was set at an alpha level of p < 0.05.

When compared to professional rugby players (Table 1), both forwards and backs from the non-professional team were shorter, had more adiposity, and possessed less muscle mass. These differences were anticipated due to the less competitive nature of their rugby environment, where such physical attributes are crucial for performance (7). The reasons for these disparities may include selection processes, living in disadvantaged socio-economic settings, genetics, the amount of training, and/or nutrition.

The use of the five-way fractionation model allowed for the quantification of important components such as muscle, bone, and adipose tissue. This enabled the calculation of the MBR, an index that can be particularly useful since muscle mass is correlated with strength and performance (21). Adipose tissue is defined as the anatomical entity, encompassing lipids, proteins, electrolytes, and water, and differs from chemically-defined fat mass which only refers to lipids (22). This explained why adiposity values were higher than percent fat values, as shown in Table 1. Considering previous findings, the fat mass calculated in the participants included in this study show resulted higher compared to elite rugby players as well as athletes from other sports teams (3, 23, 24).

Back players had a higher energy intake compared to the heavier forward players (Table 2). The median energy intake was 3,363 Kcal (range 1,220–7,982), which is lower than that of Australian rugby league players, whose median intake is 4,230 Kcal (range 2,671–6,917 Kcal) (13), presumably because the amateur players trained less. Since the PAL was not different between the two groups (Forwards 1.84, Backs 1.83), there may have been over-reporting of food intake by backs and/or under-reporting by forwards. When the ratio of energy intake to basal metabolic rate was derived, forwards had a ratio of 1.68 ± 0.67, which was less than the backs' ratio of 2.11 ± 0.63. These ratios suggest that the heavier forwards might have been underestimating their food intake, and backs overestimating it, assuming accurate food intake assessment would result in values similar to their PAL values.

Macronutrient intake (protein, carbohydrate, and fat) was higher in backs (1.8, 5.3, and 1.9 g kg⁻¹ day⁻¹, respectively) compared to forwards (1.1, 3.3, and 1.0 g kg⁻¹ day⁻¹, respectively). This discrepancy was expected since forwards were heavier and possibly under-reported their intake. The protein and carbohydrate intake for forwards were below the recommended values of 1.6 and 7–8 g kg⁻¹ day⁻¹. It may be worth considering that larger athletes might require smaller per-kilogram nutrient recommendations, or that some adjustment for lean mass could be necessary.

The median values for the whole group exceeded the RDAs for all vitamins and minerals (Table 3), except for calcium (74%), vitamin A (70%), and vitamin C (56%). For these three micronutrients, the prevalence of players consuming less than the Estimated Average Requirement (EAR) was also the highest. Although the median calcium intake was 738 mg, which is not particularly low, the recommended daily allowance (RDA) of 1,000 mg is considered by some to be excessively high and is a topic of ongoing debate (25). The low intake of vitamins A and C, along with the low fiber intake, indicated a low intake of fruits and vegetables (Figure 4). The participants of this study reported a low intake of calcium and vitamin C than professional (13) and semi-professional rugby players (26) who reported consuming 1,400–1700 mg of calcium and 150–200 mg of vitamin C.

Figure 3 showed that almost half of the daily energy intake was at dinner, while only 13% was at breakfast, with 23% of the participants used to skip breakfast. Training sessions for these players were schedule at night and were followed by dinner. This eating pattern may not optimize the balance between exercise and nutrition, as it involves insufficient intake before training and excessive consumption afterward. Although dietary periodicity studies are not very common, research including team sport athletes in Australia also found that these athletes tended to eat larger meals at night (27). Cereal and starches contributed most of the energy to the diets of these rugby players, followed by meat and eggs, sweets and sugars, and fats and oils (Figure 4). Less energy came from dairy products, and fruits and vegetables. Alcohol contributed 1% of energy intake, less than the 4%–5% reported by Lundy et al. (13), but this figure most likely underestimated real intake since the survey included a Friday before Saturday morning training. One of the impacts of a restricted budget on dietary choices was increasing the consumption of inexpensive starches, sugars, and oils, while diminishing that of higher-priced, nutrient-dense meats, dairy, fruits, and vegetables, as was found with these players, except for the high meat intake, which, although declining, is abundant in Argentina (28).

Some study limitations should be listed. For example, the 24-h dietary recall method had many pitfalls, including under-reporting by fatter individuals, and the software used for nutrient analysis might have had its own limitations; for instance, we were perplexed by the median value for iron intake, which at 66 mg was about two to three times larger than that data reported in previous studies (13, 26). Future studies should aim to obtain nutritional data from three-day weighted food records.