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

This project was approved by the Ethics Committee of the Faculty of Human Kinetics, University of Lisbon (Code: 13/2022) in May 2022. Informed consent was obtained from all participants included in this study. All related procedures were conducted in accordance with the standard of ethics outlined in the Declaration of Helsinki [18].

This study was conducted as a single cross-sectional study during two competitions in Portugal and Spain. A total of 58 international-level E and NE OAs of seven countries, including Angola (n = 1), Brazil (n = 5), Poland (n = 1), Portugal (n = 26), South Africa (n = 1), Spain (n = 22), and Sweden (n = 2), were included in this study [30 males (M) and 28 females (F); (age 41.7 ± 10.3 years: NEFOA, 25.5 ± 6.4 years: EFOA, 37.2 ± 14.6 years: NEMOA, 24.3 ± 5.0 years: EMOA)]. The main inclusion criteria for OAs were set as follows: (1) valid license in the Portuguese Orienteering Federation (FPO) [19], Spanish Orienteering Federation (FEDO) [20], or IOF [1]; (2) being in the age group of 18 to 65 years; and (3) without metabolic disease or any disease that could affect body fat and 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 OAs of different nationalities, considering the sum of registered participants in official, annual, and internationally renowned events in Portugal and Spain. Thus, a confidence level of 95% was adopted for the sampling calculation, as well as the associated critical value of 1.96 (Z-score), ± 10% error margin, and a population with homogeneous features (p = 0.8) [21], which show an “n” of 61 subjects. An a priori power analysis in terms of the omnibus test (ANOVA) was performed with a significant level of 0.05, a large effect size (0.4), and a power of 0.80 for four groups (2 genders and 2 elite classes), which show an “n” of 18 subjects for each group. A total of 45 athletes were approached, always before the competition, in each location, and the consent response rate was 25 and 33 in Spain and Portugal, respectively. Normally, the refusals occurred due to the athletes’ alleged needs to concentrate for the competition; admittedly, in orienteering, the psychological component is significant [17]. 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.

Sociodemographic data (i.e., date and country of birth, sex), training data (i.e., practice, frequency, and quantity), and NK and PAL data were collected from the orienteers using an online electronic scheme built on Google Forms© by the researchers. For all orienteers, anthropometric data were obtained during a single day, between 6:30 a.m. and 10:00 p.m., at two competitions in 2023: “Portugal “O” Meeting (POM)”, organized by FPO [19], and “35° Trofeo Internacional Murcia Costa Cálida”, organized by the FEDO [20] and Orienteering Federation of the Region of Murcia (F.O.R.M.) [22]. Both competitions were IOF [1] world ranking events, valid for the international ranking, in which only the best orienteers in the world compete in the elite category, who must be included in this ranking in order to participate. We consider that the COVID-19 restrictions in 2023, the year in which the data was collected, did not affect the regular orienteering schedule or training patterns, which were normal.

To assess the NK of the OAs, especially the concepts related to sports nutrition, we used the updated Abridged Nutrition for Sport Knowledge Questionnaire (A-NSKQ) [11]. This instrument was validated for use with athletes of different nationalities, levels of competition, and sports [10,11]. 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 specifically about sports nutrition (SNK). NK scores were expressed as percentages of correct answers obtained by the subjects in each subsection (GNK and SNK), and total nutritional knowledge (TNK) was obtained with the sum of the subsections. The level of knowledge was classified as poor (0–49%), average (50–65%), good (66–75%), and excellent (76–100%) [10]. The A—NSKQ has been shown to exhibit high construct validity (p < 0.001) with good test-to-test concordance (r = 0.80; p < 0.001) among athletes. From the original study, scores > 47% represent greater than average nutrition knowledge [10,11,12]. The languages used were English [23] and Spanish [24], official versions, and no cross-cultural adaptation was performed in any case.

The PAL was assessed based on the short version of the self-reported International Physical Activity Questionnaire—Short Form (IPAQ—SF), in two official versions [25], English and Spanish; therefore, there was no need for cross-cultural adaptation. This instrument, with validity and re-producibility tested in numerous countries [26], consists of eight open questions that allow for estimating the time spent per week, the last seven days of the assessment, 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 [25,27], the total PAL score was calculated by multiplying the 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; and vigorous activity, 8.0 METs. The sum of products found for each PA type gave origin to the total PAL score (walking + moderate PA + vigorous PA = total PAL score) [25]. Values lower than 10 min 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 min do not lead to health benefits [25]. Cases whose total PAL score exceeded 960 min (16 h per day) were considered outliers according to the IPAQ guidelines [28]; they were excluded from the analysis. Categorical and continuous IPAQ—SF data processing and analysis followed official guidelines [25]. The PAL were categorized in three levels: (1) “low”, the lowest, for those individuals who did not walk for at least 10 min, and those who did not moderate PA were considered low/inactive; (2) “moderate”, for any of the following criteria: three or more days of vigorous activity for at least 20 min per day, five or more days of moderate-intensity activity or walking for at least 30 min per day, or five 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”, for any of the following two criteria: vigorous-intensity activity on at least 3 days and accumulating at least 1500 MET—min/week or seven or more days of any combination of walking, moderate-intensity, or vigorous intensity activities achieving a minimum of at least 3000 MET—min/week [25].

The anthropometric variables of the subjects were measured according to the ISAK protocol [9] by certified levels 3 and 4 Anthropometrists [29], who adopted hygienic–sanitary care against COVID-19 [30]. Twenty-six anthropometric variables were measured for each subject, namely, four basic measurements (body mass, stature, sitting height, arm span), nine skinfold thicknesses (pectoral, according to procedures described by the American College of Sports Medicine (ACSM) [31], triceps, subscapular, biceps, suprailiac, supraspinal, abdominal, front thigh and calf), nine circumferences (neck, relaxed and contracted arm, chest, waist, hip, thigh middle, calf, ankle), and four bone breadths (biepicondylar humerus, bi-styloid, biepicondylar femur, bimalleolar). Body mass was measured using a scale to the nearest 0.1 kg (Seca, model: 7601419004; Seca Gmbh & Co. KG, Hamburg, Germany), and stature and sitting height were measured using a stadiometer to the nearest 0.1 cm (Seca, model 2131721009; Seca Gmbh & Co. KG, Hamburg, Germany). Arm span was measured using a segmometer to the nearest 0.1 cm (Cescorf, Porto Alegre, Brazil). Circumferences were taken to the nearest 0.1 cm using a measuring tape (Rosscraft Innovations, Spokane, WA, USA). Bone breadths were measured to the nearest 0.1 cm using a measuring small bone caliper (Rosscraft Innovations, Spokane, WA, USA), and skinfold thicknesses were measured to the nearest 0.5 mm using a calibrated caliper (Rosscraft Innovations, Spokane, WA, USA). Repeated measures for each parameter were collected to determine the technical error of measurement (TEM) [32]. To mark the anthropometric reference points, a segmometer was used to the nearest 0.1 cm (Cescorf, Porto Alegre, Brazil), with the aid of a dermatographic pencil. BMI was calculated as body mass in kilograms divided by the square of stature in meters (kg/m²) [33]. Body density (BD) was estimated by using specific equations for M [34] and F [35] athletes. BD was transformed into body fat (BF) percentage using equations specific for each sex published by the ACSM [31]. Bone mass (BM) and muscle mass (MM) were determined in kilograms (kg) through the methods of Martin [36] and Lee et al. [37], respectively. Anthropometric somatotyping was performed using the Heath and Carter method [38]. Further, individual somatotypes were plotted on a two-dimensional somatochart by calculating values of X (ectomorphy − endomorphy) and Y [2 × mesomorphy − (endomorphy + ectomorphy)] coordinates: somatotype dispersion distance (SDD) (distance between mean somatoplot and each individual somatotype, represented in Y distance units, that is, in terms of distances at the Y-axis of a somatoplot), somatotype dispersion mean (SDM) (average of all the somatotype dispersion distances), somatotype attitudinal distance (SAD) (distance between any two somatopoints), and somatotype attitudinal mean (SAM) (average of the SADs of each somatopoint from the mean somatopoint). The last two are three-dimensional counterparts of the SDM [38].

Further details regarding the processes adopted for anthropometric and PAL assessment are provided in Silva et al. [6].

The data collected were inputted into Microsoft Excel 2015 (Microsoft Corporation, Redmond, WA, USA). The R programming language version 4.3.1 [39] 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 were revealed as not normally distributed, non-parametric analyses were required. The measures of central tendency, namely, means (Ms) ± standard deviations (SDs) were calculated for each variable. We conduct a two-way ANOVA (Type III) in the case of normal data or Aligned Rank Transform ANOVA [40] in other cases. Post hoc multiple comparison procedures were assessed, but only the interaction term (sex–ranking) was significant; in this case, a pairwise t-test was used with Bonferroni correction. Pearson correlation coefficient (r) tests were used to calculate the relationship between the variables demographics, anthropometrics, BC, somatotype, NK, MET, and energy expenditure for PAL domain data for each sex. The results obtained following ANOVA analysis were coupled with the standardized effect size partial eta square (η²_p) and were interpreted in terms of ‘small effect size’ (0.01 ≤ η²_p < 0.06), ‘medium effect size’ (0.06 ≤ η²_p < 0.14), and ‘large effect size’ (η²_p ≥ 0.14) [41]. For the interpretation of effect size based on Pearson’s r values, the following guidelines were used: values around 0.10, 0.30, and 0.50 are commonly considered to be indicative of small, medium, and large effects, respectively [41]. The chosen statistical tests were appropriate for the data distribution and aimed to identify significant differences and correlations. The alpha level for statistical significance was established at p ≤ 0.05.

This study aimed to identify the physical characteristics (anthropometric, somatotype, BC) of orienteers, with rankings separated by gender, and to compare them with NK and PAL.

In sports, nutrition and performance are inextricably linked, and the assessment of NK is an important component of providing support to athletes [42,43]. For instance, Relative Energy Deficiency in Sport (RED) can be prevented through nutritional intervention in sports nutrition and/or individual athlete-centered nutrition counseling, with evidence-based information and recommendations [44,45]. Similarly, a recent intervention was conducted in female endurance athletes [46].

To the best of our knowledge, this is the first study to investigate the NK of orienteers that presented results similar to E and NE Australian team sport athletes who play Australian football, cricket, lawn bowls, soccer, or hockey [47] and E Greek handball players [48]. 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 become part of orienteering teams, as orienteers may use non-professional sources of nutrition, and studies have identified positive effects of nutritional support on NK [49,50]. We emphasize that NK is crucial for athletes to make informed dietary choices that support their training and recovery processes [13].

In general, athletes exhibit a high PAL due to the demands of their specific sport [16,51]. This was also evident in the present study, where EOAs had higher energy expenditure than NE athletes. Continuous monitoring of PAL should be considered by training teams to ensure an accurate estimation of training load, aiming to prevent overtraining in the athlete population [16].

The data show that EOAs are significantly younger and train more frequently and for longer durations each week than NEOAs; however, approximately 500 h of annual training can present at least one injury (currently a hotly debated topic in sports sciences) in the same period of time [52], causing potential losses in performance. Younger athletes often have the capacity to train more intensively and recover more quickly [2]. However, a recent systematic review and meta-analysis also found that early specialization in a sport does not necessarily facilitate later athletic excellence, and participation patterns can differ significantly between junior and senior elite athletes [53].

EMOAs tend to have a lower BF percentage than NEOAs. This is consistent with the demands of orienteering as an endurance sport, where a leaner BF can contribute to better performance [54]. Similar trends were observed in another study with Turkish EOAs [55]. Additionally, our results are similar to the findings of a study that aimed to investigate morphological parameters (body mass, stature, BMI) of 50 medalists at the WMOC 2022 [5].

The somatotype data indicate that EFOAs have a more ectomorphic (lean) and less mesomorphic (muscular) somatotype than NEOA, similar to elite triathletes [56]. This could be related to the nature of orienteering (e.g., endurance and agility), which involves navigating a variety of terrains and, therefore, may favor a lighter body type [54]. Ectomorphy can be correlated with better performance results in orienteering sports [55]; however, ectomorphs have a tendency to more intensive lipolysis; consequently, it is recommended to eat a high-carbohydrate diet and combine evenly divided portions of protein and fat [57]. Conversely, sports among high-performance athletes in water, cycling, and combat sports may favor a more mesomorphic somatotype [58]. Genetic predisposition is only one of the components that, together with other factors (e.g., training, nutritional status, psychological preparation), play a key role in the successful career of athletes [59].

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 OAs.

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 [60,61,62]. 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 [63,64].

This study boasts several strengths, including the level of the athletes and the fact that the data were collected during two important tournaments in Europe. Additionally, anthropometric measurements were meticulously gathered by ISAK-certified Anthropometrists of levels 3 and 4, ensuring a high degree of accuracy and precision in the collected data [32].

Our study has some limitations. Firstly, there was no opportunity to conduct longitudinal research. We had a relatively small, yet representative, study sample size of the target population, and we recognize that, while the outline is good (±10% margin of error, p = 0.8), there was a relatively large margin of error, which should be acknowledged as a limitation. However, despite being underpowered, the effect size is still the change in the parameter that would be of interest to other scientists/coaches for practical significance [65]. Moreover, this study did not consider multiple external factors that influence NK among OAs, such as socio-economic status, access to resources, and cultural influences. And we acknowledge that NK alone may not translate into dietary behavior changes. Furthermore, we did not evaluate dietary intake patterns. Another limitation of this study was the use of the IPAQ—SF to estimate the athletes’ energy expenditure. Although the instrument has been validated for use in various countries with different cultural contexts, research consistently shows that when compared to direct methods of assessing energy expenditure and physical activity, the IPAQ—SF overestimates the estimates [66]. Therefore, this should be acknowledged, and future investigations with OAs should consider instruments with greater accuracy, wearable technologies for long-term monitoring, and at the event itself, and objective measures of physical activity (e.g., accelerometry).