Relationship of Body Composition and Somatotype with Physical Activity Level and Nutrition Knowledge in Elite and Non-Elite Orienteering Athletes

1. Introduction

Orienteering is described as a cross-country type event, with the rules according of the International Orienteering Federation (IOF) [1], that management four Orienteering disciplines: 1) Foot orienteering (FootO); 2) Mountain Bike or MTB (MTB orienteering); 3) Ski orienteering (SkiO); and 4) Trail orienteering (TrailO).

It is well-documented that Orienteers’ performance predominantly relies on the aerobic component, interspersed with bouts of anaerobic activity [2]. Given these conditions, it is hypothesized that Orienteers experience high energy expenditure and substantial fluid and electrolyte losses during activity. Consequently, an appropriate physical profile and comprehensive nutritional support are essential by performance.

Despite Orienteering’s widespread popularity across 76 countries, including Portugal and Spain, comprehensive studies that could contribute to optimizing the performance of Orienteering athletes (OA) are still limited. This scarcity extends to research involving measurable human body features, which should be standardized according to the guidelines of the International Society for the Advancement of Kinanthropometry (ISAK) [3]. Additionally, there is a lack of studies assessing the physical activity levels (PAL) and nutritional knowledge (NK) of these athletes [4,5,6]. Thus, to the best of our knowledge, no studies have investigated these variables. Addressing this knowledge gap could significantly improve the design of exercising/training and diet programs, as well as facilitate the monitoring of both acute and chronic effects of interventions. These elements are crucial for achieving excellence in Orienteering [7].

The aims of this study were (1) to describe the anthropometric characteristics, body composition (BC), somatotype, NK, and PAL of elite (E) and non-elite (NE) OA and (2) to explore the associations between BC and somatotype according to NK and PAL.

2. Materials and Methods

2.1. Ethical Approval

This project was approved by Ethics Committee of the Faculty of Human Kinetics, University of Lisbon (FMH Code 13/2022) in May 2022. Informed consent was obtained from all participants included in the study. All related procedures were conducted in accordance with the standard of ethics outlined in the Declaration of Helsinki [8].

2.2. Participants

Fifty-eight international-level E and NE OA of seven countries, Angola (n = 1), Brazil (n = 5), Poland (n = 1), Portugal (n = 26), South Africa (n = 1), Spain (n = 22) and Sweden (n = 2), volunteered participate in the current study, 30 males (M) and 28 females (F); age: 41.7 ± 10.3 (NEFOA), 25.5 ± 6.4 (EFOA), 37.2 ± 14.6 (NEMOA), 24.3 ± 5.0 (EMOA) years. Inclusion criteria set for all subjects were being OA with valid license in Portuguese Orienteering Federation (FPO) [9] or Spanish Orienteering Federation (FEDO) [10] or IOF [1], being in the age group 18 to 65 years, without metabolic disease or any disease that could affect body fat, not having taken hormone treatment or corticoids in the three months prior to the anthropometric assessment, except for contraceptives. The sample size (n) was calculated according to the population (N) of 2500 OA of different nationalities, considering the sum of registered participants in official, annual and internationally renowned events in Portugal and Spain. Thus, confidence level of 95% was adopted to the sampling calculation, as well as associated critical value of 1.96 (Z-score), ± 10% error margin and population with homogeneous features (p = 0.8) [11], which show an “n” of 61 subjects. The rankings of the Orienteers, separated by gender, were obtained from the IOF/ World Ranking [1], accessed on 20 February 2023. An OA was classified as “NE” if they were not listed in the aforementioned rankings.

2.3. Variables and Measures

The demographic, NK and PAL data of the Orienteers were collected using an online electronic scheme built on Google Forms^© by the researchers. For all Orienteers, data anthropometric were obtained during a single day, between 6:30am-10:00pm, at two competitions 2023: “Portugal "O" Meeting” organized by FPO [9] and “35º Trofeo Internacional Murcia Costa Cálida” organized by FEDO [10] and Federation of Orienteering Sport of the Valencian Community (FEDOCV) [12].

2.4. Nutritional Knowledge Assessment

To assess NK of athletes, especially the concepts domain related to sports nutrition, we used the updated Abridged Nutrition for Sport Knowledge Questionnaire (A-NSKQ) [6]. This instrument was validated for use with athletes of different nationalities, levels of competition and sports [4,5]. The questionnaire is composed of multiple-choice questions with three or four alternative answers and just one correct answer. The questionnaire contains 35 questions, divided into two subsections. The first section contains 11 questions about general nutrition knowledge (GNK); the second section contains 24 questions about sports nutrition (SNK) specifically. Nutritional knowledge scores were expressed as percentages of correct answers obtained by the subjects in each subsection (GNK and SNK) and in the sum of the subsections (total nutritional knowledge - TNK). The level of knowledge was classified as poor (0-49%), average (50-65%), good (66-75%) and excellent (76-100%) [4].

2.5. Physical Activity Assessment

The PA was assessed based on the short version of the self-reported International Physical Activity Questionnaire – Short Form (IPAQ-SF) [13], instrument with validity and re-producibility tested in numerous countries [14], that consists of eight open questions that allow estimating the time spent per week, the last 7 days of the assess, on different PA domains (i.e. walking and physical effort from moderate to vigorous intensity) and physical inactivity (i.e. sitting). Considering that the IPAQ-SF data can also be used to estimate the score expressed as metabolic equivalent (MET), in minutes per week (MET-min/week) [13,15], the total PA score was calculated by multiplying METs recorded for each activity type and the volume observed for each activity type was calculated by weighting its energy requirements: walking, 3.3 METs; moderate activity, 4.0 METs; vigorous activity, 8.0 METs. The sum of products found for each PA type gave origin to the total PA score (walking + moderate PA + vigorous PA = total PA score) [13]. Values lower than 10 minutes of PA (per day) were not included in the calculation; they were re-coded to “zero’, since scientific evidence indicates that PA sessions shorter than 10 minutes do not lead to health benefits [13]. Cases whose total physical activity score exceeded 960 minutes (16 hours per day) were considered outliers; they were excluded from the analysis. Categorical and continuous IPAQ-SF data processing and analysis have followed official guidelines [13]. The PAL were categorized in three levels: 1) “low”, the more lowest, those individuals who not walking for at least 10 minutes or didn’t do moderate PA were considered low/ inactive; 2) “moderate”, any one of the following three criteria: 3 or more days of vigorous activity of at least 20 minutes per day or 5 or more days of moderate-intensity activity or walking of at least 30 minutes per day or 5 or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum of at least 600 MET-min/week; and 3) “high”, any one of the following two criteria: vigorous-intensity activity on at least 3 days and accumulating at least 1500 MET-minutes/week or 7 or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum of at least 3000 MET-minutes/week [13].

2.6. Anthropometric Measurements

Anthropometric variables of the subjects were measured according to ISAK protocol [3] by Level 3 or 4 ISAK Anthropometrist [16], and adopted hygienic-sanitary care against COVID-19 [17]. Twenty-six anthropometric variables were measured for each subject, namely: four basic measurements - body mass, height, sitting height, arms span; thickness of nine skinfolds - pectoral, according to procedures described by the American College of Sports Medicine (ACSM) [18], triceps, subscapular, biceps, iliac crest, supraspinal, abdominal, thigh and calf; nine girths - neck, relaxed arm, flexed and tensed arm, chest, waist, hips, thigh middle, calf and ankle; and four breadths - humerus, bi-styloid, femur and bimalleolar. Body mass was recorded on a portable scale to the nearest 0.1 kg (model 760, SECA gmbh & co. kg, Hamburg, Germany) and height and sitting height were measured on a stadiometer to the nearest 0.1 cm (model 213, SECA gmbh & co. kg, Hamburg, Germany). Arms span was measured using a measure tape, to the nearest 0.1 cm. Girths measurements were performed using a flexible anthropometric steel tape, to the nearest 0.1 cm (ROSSCRAFT INNOVATIONS, Spokane, USA). Breadths measurements were performed using a small bone caliper, to the nearest 0.1 cm (ROSSCRAFT INNOVATIONS, Spokane, USA), and skinfold thickness was measured using a caliper calibrated to the nearest 0.5 mm (ROSSCRAFT INNOVATIONS, Spokane, USA). To mark the anthropometric reference points was used a segmometer, to the nearest 0.1 cm (CESCORF, Porto Alegre, Brazil), with the aid of dermatograph pencil. Body Mass Index (BMI) was calculated as weight (kg) to height squared (m²) quotient [19]. Body density (BD) was estimated by using specific equations for male [20] and female [21] athletes. BD was transformed into body fat (BF) percentage using equations specific for each sex published by the ACSM [18]. Bone mass (BM) and muscle mass (MM) were determined in kg through the methods by Martin [22] and Lee et al. [23], respectively. The data from the anthropometric assessments were used to calculate the somatotype values, compared to each other, somatotype dispersion mean (SDM), somatotype dispersion distance (SDD), somatotype attitudinal mean (SAM) and somatotype attitudinal distance (SAD), according to the Heath-Carter method [24].

Further details regarding the processes adopted for anthropometric and PA assessment are provided in Silva et al. [25].

2.7. Statistical Analysis

Data collected was inputted into Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). The R programming language (version 4.3.1) [26] was used for all statistical analyses, including the Somatoplot performed. The normality of each variable was assessed using the Kolmogorov–Smirnov normality test and when the data if revealed that was not normally distributed non-parametric analyses were required. A descriptive analysis (mean and standard deviation [SD]) was calculated for all variables unless otherwise stated. T-test or Pearson correlation coefficient (r) tests were used to calculate the relationship between the variables demographics, anthropometrics, BC, somatotype, NK, MET and energy expenditure for PA domains data for each sex. The alpha level for statistical significance was established at p ≤ .05. The chosen statistical tests were appropriate for the data distribution and aimed to identify significant differences and correlations.

3. Results

Participants were 58 subjects, 23 EOA: 10 EFOA (age = 25.5 ± 6.4 yr., body mass = 59.5 ± 7.7 kg, height = 168.1 ± 6.5 cm, BMI = 21.0 ± 1.9 kg/m²) and 13 EMOA (age = 24.3 ± 5.0 yr., body mass = 65.0 ± 5.5 kg, height = 175.1 ± 6.0 cm, BMI = 21.3 ± 2.2 kg/m²), and 35 NEOA: 18 NEFOA (age = 41.7 ± 10.3 yr., body mass = 60.6 ± 8.5 kg, height = 161.3 ± 11.7 cm, BMI = 23.4 ± 3.7 kg/m²) and 17 NEMOA (age = 37.2 ± 14.6 yr., body mass = 71.5 ± 14.2 kg, height = 174.0 ± 8.8 cm, BMI = 23.6 ± 4.1 kg/m²). EOA were significantly younger (p < .01) and had a higher frequency of orienteering training (OTF) (measured in days per week) (EFOA: p = .024; EMOA: p < .01) compared to their NEOA counterparts in both sexes. Regarding the orienteering training quantity (OTQ) (measured in hours per week) only EFOA demonstrated a significantly greater volume (p = .022) than NEFOA. EMOA had a significantly shorter sitting height (p < .01) and lower abdominal skinfold thickness (p = .04) compared to NEMOA. Additionally, the girths of the neck (p = .03), chest (p = .028), and waist (p = .010) were significantly smaller in EMOA than in NEMOA. The waist girth (p = .014) was also significantly smaller in EFOA compared to NEFOA. The percentage of body fat (BF) (p = .050) in EMOA was significantly lower than in NEMOA. Mesomorphy (p = .030) in EFOA was significantly lower than in NEFOA, but the ectomorphy (p = .019) was significantly higher (Table 1; Figure 1 and Figure 2).

All significant comparisons show a large effect size (Cohen’s D), except endomorphy (EFOA x NEFOA) with a small effect size (Cohen’s D = .47), and BF and abdominal skinfold thickness (EMOA x NEMOA) with medium effect size (Cohen’s D = .76 and .79, respectively).

Although the SDM and SAM did not show significant differences, both the SDD (FOA: 4.3; MOA: 3.1) and SAD (FOA: 1.8; MOA: 1.3) exhibited values greater than 3 and 1, respectively. This indicates considerable differences in somatotypes between E and NE, for MOA (Figure 3) and FOA (Figure 4). Thus, EMOA are ectomorphic mesomorph while NEMOA are balanced mesomorph and EFOA are central and NEFOA are endomorphic mesomorph.

Significant differences (p = .025) were also observed in the SNK among EMOA and NEMOA, with the former group achieving a higher percentage of correct responses. In the case of TNK, EOA of both sexes scored significantly higher (EFOA: p = .042; EMOA: p = .029) than their NEOA counterparts.

The Table 2 shows the association of NK with BC, somatotype, MET, and energy expenditure for Orienteers. The most significant findings in FOA include a negative correlation between age and SNK (r = −.40, p = .034) and TNK (%) (r = −.41, p = .030). Furthermore, OTF and OTQ exhibit a positive correlation with MET-min/week, kcal/week, GNK, SNK and TNK, all with a p-value less than 0.05. A significant negative correlation is also observed between BF and MET-min/week (r = −.39, p = .038), BM and MET-min/week (r = −.40, p = .033), endomorphy and SNK (r = −.38, p = .045). Lastly, a positive correlation was observed between GNK and SNK (r = .50, p = .006). Among MOA the most significant findings include a negative correlation between age and MET-min/week (r = −.49, p = .010), kcal/week (r = −.46, p = .016) and SNK (r = −.40, p = .029). Furthermore, OTF and OTQ exhibit a positive correlation with MET-min/week and kcal/week with a p-value less than 0.05. A significant negative correlation is also observed between BF and MET-min/week (r = −.48, p = .011) and kcal/week (r = −.42, p = .029), MM and MET-min/week (r = −.45, p = .020), BMI and MET-min/week (r = −.57, p = .002) and kcal/week (r = −.50, p = .008), endomorphy and MET-min/week (r = −.44, p = .021), mesomorphy and MET-min/week (r = −.47, p = .013) and kcal/week (r = −.45, p = .018). Ectomorphy show positive correlation with MET-min/week (r = .59, p = .001) and kcal/week (r = .54, p = .004). Lastly, a positive correlation was observed between GNK and SNK (r = .55, p = .002).

4. Discussion

This study aimed to identify physique characteristics (anthropometric, somatotype, body composition) of Orienteers with rankings separated by gender and to compare them across with NK and PAL

In sport, nutrition and performance are inextricably linked, and the assessment of NK is an important component of providing support to athletes [27,28]. For instance, Relative Energy Deficiency in Sport (REDs) can be prevented through nutritional intervention in sports nutrition and/ or individual athlete-centered nutrition counseling, with evidence-based information and recommendations [29,30]. Similarly, a recent intervention has been conducted in female endurance athletes [31].

To the best of our knowledge, this is the first study to investigate the NK of Orienteers that presented results similarly to elite and non-elite Australian team sport athletes, playing Australian football, cricket, lawn bowls, soccer, or hockey [32]. Therefore, although athletes may have GK of healthy eating, we understand that they may need specific knowledge of sports nutrition. Thus, we recommend that nutritionists be part of the Orienteering teams, as Orienteers may use non-professional sources of Nutrition, and studies have identified positive effects of nutritional support on NK [33,34].

The data shows that EOA are significantly younger and train more frequently and for longer durations each week than NEOA ones, however, with approximately 500 hours of annual training, OA have injury incidence rates of around 2 injuries/1000 hours of training [35]. This is consistent with findings in other endurance sports, where younger athletes often have the capacity to train more intensively and recover more quickly [2]. A recent systematic review and meta-analysis also found that early specialization in a sport does not necessarily facilitate later athletic excellence, and that participation patterns can differ significantly between junior and senior elite athletes [36].

EMOA tend to have a lower BF percentage than NEOA ones. This is consistent with the demands of Orienteering as an endurance sport, where a leaner BF can contribute to better performance [37]. Similar trends have been observed in other endurance sports such as long-distance running and cycling. Additionally, our results are similar to the findings of a study that aimed to investigate morphological parameters (body mass, height, BMI) of 50 medallists at the World Masters Orienteering Championships (WMOC) 2022 [38].

Somatotype data indicate that EFOA have a more ectomorphic (lean) and less mesomorphic (muscular) somatotype than NEOA. This could be related to the nature of the sport, which involves navigating a variety of terrain and therefore may favour a lighter body type [37]. Conversely, sports such as weightlifting or rugby may favour a more mesomorphic somatotype.

The characteristics of successful Orienteers reflect the multifaceted demands of the sport, which combines physical endurance, navigational skills, and strategic decision-making. These demands differentiate Orienteering from many other sports, and further research could provide more insights into the optimal training strategies, physiological profiles, and nutritional needs for OA.

These results can be useful for health and sports performance professionals working with Orienteers and can help inform nutritional intervention strategies. However, more research is needed to further explore these relationships and to develop effective evidence-based interventions. These findings are consistent with previous studies that have demonstrated the importance of NK in sports performance [39,40,41]. Moreover, these results underscore the need for greater nutritional education and support for athletes, especially in endurance sports where nutrition can have a significant impact on performance [42,43].

5. Conclusions

Extensive material has been collected from 58 competitive Orienteering athletes of different nationalities. A questionnaire online was used to collect the demographic, physical activity level and nutrition knowledge data of the Orienteers and the study is its cross-sectional nature.

Diet quality scores or indices may therefore be a useful tool for investigating the relationship between nutrition knowledge and dietary intake. The specific influence of nutrition knowledge on dietary intake is an important research question. This study contributes to the understanding of the physical and nutritional profiles of Orienteers, providing valuable insights for coaches and sports scientists. Future research should explore the longitudinal effects of targeted nutritional interventions on Orienteering performance.