Abstract
Long-distance swimmers exert energetic, physiological, and neuromuscular demands that must be matched with adequate body composition to improve their performance in long-distance swimming. Objectives: This review aims to compile all available information on energetic and physiological demands, optimal body composition, nutrition, and ergogenic supple- ments in long-distance swimming. This will provide an understanding of the specific challenges and needs of this sport and will help swimmers and coaches design more effective training and nutrition plans to optimise performance and achieve their goals. Methods: Databases such as Web of Science, SciELO Citation Index, MEDLINE (PubMed), Current Contents Connect, KCI-Korean Journal Database, and Scopus were searched for publications in English using keywords such as swimming, endurance, energy demands, physiological demands, nutrition, body composition, and ergogenic aids, individually or in combination. Results: There is convincing evidence that several physical indicators, such as propulsive surface area, technical, such as stroke rate, and functional, such as hydration strategies, are related to swimming performance and body composition. Each athlete may have a specific optimal body fat level that is associated with improved sporting performance. The nutritional needs
and ergogenic aids, individually or in combination. Results: There is convincing evidence that several physical indicators, such as propulsive surface area, technical, such as stroke rate, and functional, such as hydration strategies, are related to swimming performance and body composition. Each athlete may have a specific optimal body fat level that is associated with improved sporting performance. The nutritional needs of open water swimmers during competition are quite different from those of pool swimmers. Conclusions: Swimmers with an adequate physique have a high body muscle mass and moderately related anaerobic strength both on land and in the water. These general and specific strength capacities, which are given by certain anthropometric and physiological characteristics, are seen throughout the work, as well as ergogenic and nutritional strategies, which have an important impact on long-distance swimming performance. Keywords:swimming; endurance; energy demands; physiological demands; nutrition; body compo- sition; ergogenic aids 1. Introduction Long-distance swimming places exceptional physiological and/or psychological de- mands on swimmers [1], both during competitions and training [2]. These demands are dependent on energetic factors that condition aerobic endurance, and are also influenced by physiological factors such as muscular strength or anaerobic power, anthropometric factors such as height or body composition, nutritional factors [3], or nervous factors such as neuro- muscular coordination [4], all of which are described in the scientific literature [5,6]. Special mention should also be made of the ergonomic support for long-distance swimming [7]. From a physiological point of view, in swimming, among all the performance factors mentioned above, the aerobic system is the energy system that is most in demand in this type of event [7], although the anaerobic lactic energy system is also activated, depending on the type of event. However, in prolonged cyclic events such as this one, the contribution Nutrients2024,16, 3949.
Nutrients2024,16, 3949 2 of 38 of this system decreases [8] and is only activated at times of high demand. Therefore, performance improvements must come from the development of the aerobic and, to a lesser extent, the anaerobic energy system [9]. Similarly, maximal oxygen uptake (VO2max) in swimming is affected by external conditions that create different metabolic and biomechanical scenarios that affect energy demands [10]. In addition, factors such as horizontal body position [10,11], which increases pressure and reduces blood flow and muscle perfusion [12], as well as having less muscle mass in the upper body, which has more fast twitch fibres than the lower body [13], can slow down the VO2 response [10] and influence energy demands. However, all energy systems contribute in some way to exercise intensity. Each system is best adapted to different stimuli [14]. Therefore, all energy systems are important in determining performance. It has also been observed that swimming speed and distance determine these intensities. Long-distance swimmers are used to swimming at low to moderate speeds [15,16], maintaining a high volume and low to moderate intensity. This is related to specific anthropometric parameters. Open water swimmers tend to be shorter and lighter, with less lean muscle mass than pool swimmers [15,17,18]. This may be because less absolute power is needed to complete open water events compared to shorter distances [19]. It may also be because less skilled swimmers have traditionally competed in open water events. Nutritional recommendations for training and competing in open water are based on recommendations for pool swimming or extrapolated from other sports with similar physi- ological requirements [19]. In this type of competition, nutrition should focus on optimising hydration and maintaining glycogen stores [19]. During the event, swimmers rely on their own sources of fuel and fluids, only using feed zones when tactically appropriate. As the duration of the race is extended, feeding zones during long-distance swimming become more important to reduce the stress associated with prolonged environmental exposure, as it is important that the amount of calories supplied to the athlete during intense training is sufficient to avoid a relative energy
sources of fuel and fluids, only using feed zones when tactically appropriate. As the duration of the race is extended, feeding zones during long-distance swimming become more important to reduce the stress associated with prolonged environmental exposure, as it is important that the amount of calories supplied to the athlete during intense training is sufficient to avoid a relative energy deficiency [20]. Although depending on the duration of the test, a significant reduction in subcutaneous adipose tissue may be observed throughout the test [21,22]. However, the literature on this type of test and its relationship to energy expenditure is not extensive [23]. On the other hand, the authors have studied nutritional supplements that can improve performance in long-distance swimming [24]. Some products can increase strength and help reduce metabolic acidosis [25], which is of particular interest to open water swimmers. Caffeine is beneficial for aerobic activities [26,27] and creatine for anaerobic sprints [28,29]. However, ephedrine and pseudoephedrine have detrimental effects on the cardiovascular system [30];β-hydroxy-β-methyl butyrate may be useful for untrained individuals [31,32], while pyruvate does not improve performance [33]. After reviewing all these aspects, the authors have not found any comprehensive review that analyses all the factors that can influence performance in long-course swimming events. Therefore, assessing how each of these elements affects performance in this type of event is crucial to better understand the phenomenon and to develop more effective training and preparation strategies. Unfortunately, the available scientific literature lacks a comprehensive approach that integrates all these elements in a systematic and rigorous way. This difficulty represents an opportunity for future review and meta-analysis studies to address this need and provide a more complete picture of the determinants of performance in long-course events. Thus, our aim is to compile all available information on energy requirements, physio- logical requirements, body composition, nutrition, and supplementation in long-distance swimming. This will help us to understand the challenges and needs of this sport. It will also allow us to discover aspects that can help swimmers and coaches create effective training and nutrition plans to improve performance and achieve their goals.
compile all available information on energy requirements, physio- logical requirements, body composition, nutrition, and supplementation in long-distance swimming. This will help us to understand the challenges and needs of this sport. It will also allow us to discover aspects that can help swimmers and coaches create effective training and nutrition plans to improve performance and achieve their goals.
Nutrients2024,16, 3949 3 of 38 2. Materials and Methods 2.1. Sources of Information This article is a narrative review focusing on considerations related to open water performance. The review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines [34]. The PICOS model was used to determine the inclusion criteria referred to in [35] (Figure).Nutrients 2024, 16, x FOR PEER REVIEW 3 of 40 2. Materials and Methods 2.1. Sources of Information This article is a narrative review focusing on considerations related to open water performance. The review was conducted following the Preferred Reporting Items for Sys- tematic Review and Meta-Analyses (PRISMA) guidelines [34]. The PICOS model was used to determine the inclusion criteria referred to in [35] (Fig- ure 1). Figure 1. PICOS models. The studies included in this review had to meet certain criteria. (I) The study popu- lation had to be swimmers. The articles had to examine energy needs, physiological needs, body composition, nutrition, and ergogenesis in swimming (II). Study designs had to be non-randomised (III). Relevant studies were selected for the review (IV). Exclusion criteria were applied to experimental research protocols. (i) Studies with participants from other disciplines or with previous disorders were excluded (ÁM-O). (ii) Articles on other sport populations, abstracts, non-peer-reviewed articles, and book chapters were also excluded (ÁM-O). (iii) Finally, studies unrelated to energy demands, physiological demands, body composition, nutrition, or ergogenic aids were excluded (ÁM-O). Author ÁM-O conducted structured electronic searches of the scientific literature in various databases, such as Web of Science (WOS), SciELO Citation Index, MEDLINE (Pub- Med), Current Contents Connect, KCI-Korean Journal Database, and Scopus. The search included a combination of the following keywords: (( swimming [MeSH Terms] OR swimming [All Fields]) OR ( ultra endurance [MeSH Terms] OR ultra endurance [All Fields]) AND ( energy [MeSH Terms] OR energy [All Fields]) AND ( physiology [MeSH Terms] OR physiology [All Fields]) OR ( body composition [MeSH Terms] OR body composition [All Fields]) OR ( nutrition [All Fields] OR nutrition [All Fields])) AND ( ergogenics [MeSH Terms] OR ergogenic [All Fields]). These keywords were chosen based
endurance [MeSH Terms] OR ultra endurance [All Fields]) AND ( energy [MeSH Terms] OR energy [All Fields]) AND ( physiology [MeSH Terms] OR physiology [All Fields]) OR ( body composition [MeSH Terms] OR body composition [All Fields]) OR ( nutrition [All Fields] OR nutrition [All Fields])) AND ( ergogenics [MeSH Terms] OR ergogenic [All Fields]). These keywords were chosen based on the opinion of the authors (ÁM-O, JM-A and JC-G), the scientific literature re- view, and controlled vocabulary such as Medical Subject Headings (MeSH). The search was not restricted by publication date but was filtered to include only English language studies in humans. The search was not restricted by publication date but was filtered to retrieve only human studies written in English. The last search was conducted on 4 Sep- tember 2024. To analyse and discuss the most relevant articles in the field, we also exam- ined the articles from the reference lists of the articles retrieved in the search (snowball strategy [36]). 2.2. Study Selection Publication titles and abstracts were identified using the search strategy described above and cross-referenced to identify duplicates (ÁM-O and JC-G). All trials assessed for eligibility and classified as relevant (ÁM-O and JC-G) were retrieved. In addition, the ref- erence section of all relevant articles was examined [37]. From the information contained Figure 1.PICOS models. The studies included in this review had to meet certain criteria. (I) The study popula- tion had to be swimmers. The articles had to examine energy needs, physiological needs, body composition, nutrition, and ergogenesis in swimming (II). Study designs had to be non-randomised (III). Relevant studies were selected for the review (IV). Exclusion criteria were applied to experimental research protocols. (i) Studies with participants from other disciplines or with previous disorders were excluded (ÁM-O). (ii) Articles on other sport populations, abstracts, non-peer-reviewed articles, and book chapters were also excluded (ÁM-O). (iii) Finally, studies unrelated to energy demands, physiological demands, body composition, nutrition, or ergogenic aids were excluded (ÁM-O). AuthorÁM-O conducted structured electronic searches of the scientific literature in various databases, such as Web of Science (WOS), SciELO Citation Index, MEDLINE
disorders were excluded (ÁM-O). (ii) Articles on other sport populations, abstracts, non-peer-reviewed articles, and book chapters were also excluded (ÁM-O). (iii) Finally, studies unrelated to energy demands, physiological demands, body composition, nutrition, or ergogenic aids were excluded (ÁM-O). AuthorÁM-O conducted structured electronic searches of the scientific literature in various databases, such as Web of Science (WOS), SciELO Citation Index, MEDLINE (PubMed), Current Contents Connect, KCI-Korean Journal Database, and Scopus. The search included a combination of the following keywords: ((‘swimming’ [MeSH Terms] OR ‘swimming’ [All Fields]) OR (‘ultra endurance’ [MeSH Terms] OR ‘ultra endurance’ [All Fields]) AND (‘energy’ [MeSH Terms] OR ‘energy’ [All Fields]) AND (‘physiology’ [MeSH Terms] OR ‘physiology’ [All Fields]) OR (‘body composition’ [MeSH Terms] OR ‘body composition’ [All Fields]) OR (‘nutrition’ [All Fields] OR ‘nutrition’ [All Fields])) AND (‘ergogenics’ [MeSH Terms] OR ‘ergogenic’ [All Fields]). These keywords were chosen based on the opinion of the authors (ÁM-O, JM-A and JC-G), the scientific literature review, and controlled vocabulary such as Medical Subject Headings (MeSH). The search was not restricted by publication date but was filtered to include only English language studies in humans. The search was not restricted by publication date but was filtered to retrieve only human studies written in English. The last search was conducted on 4 September 2024. To analyse and discuss the most relevant articles in the field, we also examined the articles from the reference lists of the articles retrieved in the search (snowball strategy [36]). 2.2. Study Selection Publication titles and abstracts were identified using the search strategy described above and cross-referenced to identify duplicates (ÁM-O and JC-G). All trials assessed for eligibility and classified as relevant (ÁM-O and JC-G) were retrieved. In addition, the reference section of all relevant articles was examined [37]. From the information contained in the full articles, inclusion and exclusion criteria were used to select eligible trials for
Nutrients2024,16, 3949 4 of 38 insertion into this systematic review. There was no disagreement between AM-O and JC-G regarding the eligibility of studies. 2.3. Data Extraction After applying the inclusion/exclusion criteria to each study, the following data were extracted (ÁM-O): study source (author/authors and year of publication); sample popu- lation, indicating the number of participants; methods; intervention characteristics; and significant differences between study groups. After data extraction, the included studies were grouped according to the following criteria: (1) energy demands; (2) physiological demands; and (3) the relationship between these demands and QC and nutrition, as well as performance aids. To minimise errors, data extraction and group formation were discussed among the authors until a final consensus was reached (ÁM-O, JM-A, and JC-G). It is important to note that the process of data extraction and clustering was not a straightforward task as some studies lacked certain information or had inconsistencies in their reporting. However, by carefully reviewing the studies and discussing discrepancies between authors, a comprehensive understanding of the relevant data was achieved. Furthermore, it is worth mentioning that the studies included in this analysis were selected based on rigorous criteria, such as the use of randomised controlled trials or the inclusion of a control group. This ensured that the studies were of high quality and provided reliable information for the analysis. Overall, the process of data extraction and pooling played a crucial role in the analysis of the studies as it allowed for a systematic and comprehensive examination of relevant data. Through this process, important patterns and trends were identified, providing valuable insights into the relationship between energy demands, QC, nutrition, and performance. 2.4. Assessing the Quality of Experiments: Risk of Bias and Levels of Evidence To carefully consider the possible limitations of the included studies to obtain reli- able conclusions, following the Cochrane Collaboration Guidelines [38], the statement Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guide- lines for reporting observational studies [39] (Figure) was used to assess the quality of the publications.Nutrients 2024, 16, x FOR PEER REVIEW 5 of 40 Figure 2. Risk of bias graph.
possible limitations of the included studies to obtain reli- able conclusions, following the Cochrane Collaboration Guidelines [38], the statement Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guide- lines for reporting observational studies [39] (Figure) was used to assess the quality of the publications.Nutrients 2024, 16, x FOR PEER REVIEW 5 of 40 Figure 2. Risk of bias graph. Green for negligible risk of bias, yellow for unclear risk of bias, and red for substantial risk of bias. Shows the overall risk of bias for each domain. For example, the length of the green rectangle means the number of studies assessed as minimal risk of bias. Table 1. Summary of risk of bias, indicating the risk of bias for each domain in each study. Random sequence generation (selection bias) Allocation concealmen t (selection bias) Blinding of participants and personnel (performance bias) Blinding of outcome assessment (detection bias) (patient-reported outcomes) Blinding of outcome assessment (detection bias) (all-cause mortality) Incomplete outcome data (attrition bias) (short-term [2–6 weeks]) Incomplete outcome data (attrition bias) (long term [>6 weeks]) Selective reporting (reporting bias) Zamparo 2005 [18]+ + + + ? + ? + Knechtle 2008 [41]? ? + + + + ? + Knechtle 2010 A [42]? ? + + + + ? ? Knechtle 2010 B [43]? ? + + + + ? + Pyne 2014 [2]+ + + ? + ? + + Shaw 2014 [19]+ + + ? + ? + ? VanHeest 2014 [44]+ + + ? + ? + ? Domínguez 2017 [7] - ? + ? + ? + ? Zamparo 2020 [45] - ? + ? + ? + ? Jiménez-Alfageme 2022 [25]? + + + + ? + ? Ben-Zaken 2022 [1]- ? + ? + - ? ? Green represents a negligible risk of bias; yellow represents an unclear risk of bias; red represents a substantial risk of bias. The level of evidence of the chosen studies was determined using the Oxford quality scoring system (Table 2), a procedure widely used worldwide [46,47]. This tool for as- sessing the quality of clinical studies has become
- ? ? Green represents a negligible risk of bias; yellow represents an unclear risk of bias; red represents a substantial risk of bias. The level of evidence of the chosen studies was determined using the Oxford quality scoring system (Table 2), a procedure widely used worldwide [46,47]. This tool for as- sessing the quality of clinical studies has become an international standard of reference [48], allowing the methodological quality of trials to be assessed in an objective and stand- ardised manner, which in turn facilitates the interpretation and comparison of the results obtained. This instrument is especially relevant in the context of systematic reviews and meta-analyses, where the assessment of the quality of the included studies is essential for the validity of the conclusions. 0 25 50 75 100 Selective reporting (reporting bias) Incomplete outcome data (attrition bias) (long term [>6 weeks]) Incomplete outcome data (attrition bias) (short term [2–6 weeks]) Blinding of outcome assessment (detection bias) (all-cause mortality) Blinding of outcome assessment (detection bias) (patient-reported outcomes) Blinding of participants and personnel (performance bias) Allocation concealment (selection bias) Random sequence generation (selection bias) % Low risk of biasUnclear risk of biasHigh risk of bias Figure 2.Risk of bias graph. Green for negligible risk of bias, yellow for unclear risk of bias, and red for substantial risk of bias. Shows the overall risk of bias for each domain. For example, the length of the green rectangle means the number of studies assessed as minimal risk of bias. Two authors (AM-O and JC-G) independently assessed methodological quality and risk of bias with any disagreement resolved by assessment by a third party (JM-A), with the Cochrane Collaboration Guidelines [38]. The following scale was used to classify study quality: (1) good quality (>14 points, low risk of major or minor bias); (2) fair quality (7–4 points, moderate risk of major bias); and (3) poor quality (<7 points, high risk of major bias) [39]. The checklist items were classified into different domains: random sequence genera- tion (selection bias), allocation concealment (selection bias), blinding of participants and
points, low risk of major or minor bias); (2) fair quality (7–4 points, moderate risk of major bias); and (3) poor quality (<7 points, high risk of major bias) [39]. The checklist items were classified into different domains: random sequence genera- tion (selection bias), allocation concealment (selection bias), blinding of participants and
Description
A review of nutritional strategies for long-distance swimming performance.