Dietary Intake According to the Evolution of the Resting Metabolic Rate and Body Composition of an Elite Olympic Athlete over a Macrocycle: A Case Study

Abstract

Monitoring physiological parameters is vital for tracking swimmers’ progress and performance. This study examines an elite male swimmer’s nutrition during his preparation for the 2024 Paris Olympics, considering his metabolic rate and body composition. His resting energy needs (2905 ± 407.99 kcal/day) were measured using indirect Cosmed K5, calorimetry, and body composition determined through skinfold measurements. Nutrition plans were developed using software, varying with his training intensity—providing 2910 ± 379 kcal/day on rest days, and 4238 ± 562 kcal/day on intense days. The analysis of the correlations between key variables revealed strong and diverse interactions among anthropometric, metabolic data, and energy substrates. Thus, weight exhibited a very strong positive correlation with lean mass (FFM), indicating that higher weight is associated with increased lean mass. Conversely, the moderate correlation between weight and body fat percentage suggests a weaker association. The amount of skin folds accurately reflects the body fat percentage. Ensuring that a high-energy dietary intake aligned with his actual needs throughout the season was crucial for sustaining performance. Experimenting with fueling and recovery tactics during smaller competitions enabled the athlete to meet energy and nutrient demands at the elite level.

1. Introduction

The 1500 m event is the longest race in competitive swimming, aside from open-water events, and was first introduced at the Olympic Games in London in 1908, where Henry Taylor won with a time of 22:48:40. Over the past 115 years, the qualifying times for the Olympics have improved by nearly eight minutes, alongside significant advancements in sports science and performance enhancement methods. Achieving a sub-15 min time in the 1500 m freestyle has become a prerequisite for success in international competitions. As a result, efforts have focused on understanding and addressing the optimal fueling and nutritional requirements for athletes during the competitive season [1].

Therefore, several studies suggest that developing optimal nutritional strategies supports swimming performance. Measuring resting metabolic rate (RMR) and body composition throughout the season may allow nutritionists and clinicians to propose better practical interventions [2,3]. Indirect calorimetry represents the gold standard measurement of the RMR by assessing oxygen consumption and carbon dioxide production. It accounts for 60–70% of the total energy expenditure [4]. Thus, incorporating RMR testing into an athlete’s routine is crucial for accurately determining energy needs and plays a vital role in maintaining optimal body fat levels, developing effective nutritional strategies, ensuring sufficient energy availability for training and competition, promoting metabolic function, and reducing symptoms of fatigue [5,6].

Poor (imbalanced) nutrition in long-distance swimming events can significantly affect performance, recovery, and overall health. By examining comparative data from other endurance sports, such as athletics, cycling, and cross-country skiing, we can better understand the specific implications for swimmers.

Energy deficiency and glycogen depletion. Long-distance swimming requires sustained energy expenditure in a thermoregulated aquatic environment, where body heat dissipates rapidly. Insufficient caloric intake can lead to premature fatigue, reduced stroke efficiency, and the inability to maintain pace [7]. Glycogen stores serve as a primary fuel source during high-intensity intervals and sustained efforts in long-distance swimming. Suboptimal carbohydrate intake results in rapid glycogen depletion, diminished endurance capacity, and increased reliance on less efficient fat metabolism. In endurance running, energy deficits are linked to glycogen depletion [8]. Similarly, in cycling, insufficient energy intake reduces acceleration power during long races [9]. For swimmers, these energy gaps are exacerbated by the additional caloric demands of water resistance and thermoregulation [10].

Hydration. Swimmers may underestimate fluid loss, as sweat is less noticeable in water. Suboptimal hydration impacts thermoregulation, muscle function, and cognitive focus, leading to impaired stroke mechanics and slower lap times. In cycling and running, dehydration is directly associated with cardiovascular strain and increased body temperature. While core temperature is less of a concern in swimming, dehydration still affects cardiovascular efficiency, a critical factor for sustained effort [11].

Electrolyte imbalances. Electrolyte loss occurs even in water, and inadequate replenishment can cause cramps, impair muscle function, and reduce neuromuscular coordination, compromising stroke efficiency [12]. Runners and cyclists often rely on electrolyte-rich drinks to prevent muscle cramps. Swimmers face unique challenges, as the aquatic environment can mask the dehydration symptoms, delaying corrective actions [13,14].

Imbalanced (suboptimal or inadequate) nutrition has severe implications for performance in long-distance swimming, compounded by the sport’s unique environmental and physiological demands. Comparative insights from athletics and cycling emphasize the critical importance of appropriate calorie, carbohydrate, protein, electrolyte, and hydration strategies tailored to the specific needs of swimming. Ensuring optimal nutrition not only enhances performance but also promotes recovery and long-term athletic health.

It is well documented that the energy expenditure of swimmers is significantly influenced by body size and body composition and is primarily dependent on lean mass [15]. However, given their heightened energy demands, particularly during periods of intensified training, providing sufficient calories to meet the physiological requirements is critical for maintaining performance [16]. Moreover, energy needs when swimming are also influenced by factors such as training and competition volume, growth, and the potential needs to adjust body composition [17]. The high training volumes undertaken by swimmers, especially for long-distance events, can be accompanied by the depletion of energy stores and impairment of the recovery process. Also, with training schedules often involving multiple training sessions per day and recovery windows that are generally short in duration (at least 8 h between sessions per day), swimmers need to optimally refuel in order to sustain subsequent exercise bouts [18].

Therefore, appropriate nutritional strategies are essential to support post-exercise recovery. Consuming dietary carbohydrates and proteins immediately after exercise provides the substrates needed to promote glycogen resynthesis and muscle protein synthesis [19]. This is especially important given that competitive formats may require athletes to swim the same event on consecutive days, which can make it challenging to match energy intake with energy expenditure, thus impairing proper fueling and post-race recovery [10].

Adjusting the total energy intake to align with the physiological requirements of the athletes, while matching the dietary macronutrient consumption to meet general fuel needs, performance goals, physique manipulation, and recovery, can optimize swimming performance and ultimately influence competition outcomes [10]. Studies regarding dietary intakes designed to align with the total and resting energy expenditure of elite swimmers are limited. Therefore, the aim of this case study is to present the dietary intake during a macrocycle for an elite male swimmer, linked to measured RMR, to highlight the increased nutritional needs of swimming and the challenges of meeting daily nutritional requirements.

2. Materials and Methods

2.1. Conceptual Framework

This research used a descriptive, longitudinal case study approach, focusing on a specific case to illustrate or exemplify a detailed and concrete method of training an Olympic athlete. The literature faces a shortage of detailed longitudinal studies that track specific changes in the mentioned parameters at the individual level, in real time, over the course of a complete training cycle. Additionally, our bibliographic review reveals a limited approach to directly correlating dietary adjustments with the unique physiological responses of elite athletes.

Thus, the use of a case study can be justified by the following arguments:

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It provides an individualized analysis: this case study enables a detailed examination of an athlete under specific conditions (an elite Olympic athlete). This is fundamental for understanding unique physiological responses, which may be concealed in group studies;

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It documents real-time changes: this case study tracks changes in real time over an entire macrocycle, providing a detailed temporal perspective that is rarely captured in other types of studies;

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It validates personalized interventions: by investigating a single case, the effectiveness of personalized dietary adjustments based on measured physiological parameters can be validated, contributing to a deeper understanding of causal relationships;

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Elite Olympic athletes represent a distinct subgroup with unique physical characteristics and physiological needs, often under-represented in generalized research.

This type of case study entails collecting data over an extended period (i.e., 1 year and 3 months) to observe the subject’s evolution or changes in independent variables over time.

2.2. Presentation of Athlete’s Characteristics

The male swimmer involved in this investigation is a member of the national swimming squad and was ranked among the top 100 swimmers in the world during the study period. His highest ranking was achieved in 2023 (60th), with a time of 15:00:51 min, earning 904 FINA (Fédération Internationale de Natation Amateur) points and qualifying for the Paris 2024 Olympic Games. His personal characteristics include the following: 18 years old, body mass (BM): 80.5 kg, height: 189.5 cm, arm-span: 193 cm, fat free mass (FFM): 74.06 kg, fat mass (FM): 6.44 kg, body fat: 8%, and VO₂ max: 65.1 mL/min⁻¹/kg⁻¹.

Since the athlete was considered a minor at the beginning of this study, verbal and written consent was obtained from the legal guardian of the swimmer, and upon reaching 18 years of age, he was also asked to provide verbal and written consent to participate in this study.

2.3. Study Design

This study used a retrospective longitudinal observational design and was conducted between May 2022 and September 2023 (

Table 1. Timeline of conducted procedures.

2022								2023
May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep
RMR		INT		INT	RMR		INT			RMR	NAT		NAT	INT		INT
		RMR					RMR				RMR		RMR	RMR
BC	BC	BC	BC		BC	BC	BC	BC		BC	BC	BC	BC	BC	BC

Legend: RMR—resting metabolic rate; BC—body composition; INT—international competition; NAT—national competition.

). The following assessments were performed to monitor the athlete’s evolution in relation to his competition performance (Table 1): resting metabolic rate (RMR), body composition (BC), and anthropometric measurements. All procedures were assessed on the same day.

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Research Ethics Committee of the National University of Physical Education and Sport in Bucharest. Clearly, a balance was sought between protecting individual and institutional identity and integrity and providing contextualized information about the case. In addition, as previously mentioned, the use of a descriptive case study approach allowed for a detailed and concrete examination of the phenomenon, rather than relying on the anonymous specificity of a context.

2.4. Resting Metabolic Rate Measurement

Seasonal variations in the resting metabolic rate (RMR) provided valuable insights into how to properly fuel the athlete before major competitions and facilitate recovery, removing the guess work associated with RMR equations. RMR was measured each time early in the morning, using Cosmed K5 (Rome, Italy). Before each test, the machine was calibrated according to the manufacturer’s instructions to ensure validity and accuracy. Prior to each measurement, the athlete was asked to undertake the test in particular conditions: in a fasted state (for at least 8 h), abstain from caffeine consumption (at least 8 h), and avoid high-intensity physical activity (at least 24 h) [20].

During the test, the athlete lay in a supine position on a mat in a quiet, dark room. Due to logistical restrictions and natural season changes, the ambient temperature could not be controlled, which may be considered a limitation of this study. The athlete wore a VO₂ face mask (Rome, Italy) without an inspiratory valve, selected based on the athlete’s face size, and the measurement lasted 16 min. During the RMR tests, the athlete wore different types of clothing, depending on room temperature and season, ranging from sport clothes (swimsuit, t-shirts, shorts, and training shoes) to warmer sports attire, including hoodies, trousers, and training shoes.

2.5. Anthropometric and Body Composition Assessment

All anthropometric and body composition indices were assessed, including body mass (kg), body height (cm), body mass index (BMI), body fat percentage (BF%), fat mass (FM), fat-free mass (FFM), and the sum of 8 skinfolds (mm). The body fat percentage (%) was calculated using the Carter equation for men: [BF% 0 2.585 + 0.1051 × ∑ X], where X represents the sum of the tricipital, bicipital, subscapular, iliac-crest, thigh, and calf skinfold thicknesses [21]. The selection of the Carter equation for calculating body fat percentage is based on its empirical validation and comparative effectiveness over other models. The Carter equation, primarily reliant on skinfold measurements, has been shown to provide a reliable estimate of body fat percentage, particularly across diverse populations. This adaptability is crucial, as body composition assessment tools must be relevant to various demographic groups to ensure accuracy. One key advantage of the Carter equation is its strong correlation with more advanced body composition assessment methods, such as dual-energy X-ray absorptiometry (DXA). Studies have demonstrated that skinfold thickness measurements, integral to the Carter equation, offer better predictive accuracy for body fat percentage compared to simpler anthropometric variables like body mass index (BMI) [22]. This is particularly significant because BMI often fails to differentiate between fat mass and lean mass, potentially leading to misclassification of an individual’s body fat status [23]. Furthermore, the Carter equation has been validated across various ethnic groups, enhancing its applicability. Research indicates that the equation performs well in different populations, including adolescents and adults, making it a versatile tool for clinicians and researchers [24]. Accurate estimation of body fat percentage is critical, as it is associated with numerous health outcomes, including obesity-related diseases [25].

Another significant factor favoring the Carter equation is its practicality. The method requires minimal equipment—primarily skinfold calipers—making it accessible for use in both clinical settings and fieldwork. This contrasts with more complex methods, such as bioelectrical impedance analysis (BIA) or DXA, which may not be readily available in all contexts [26]. The simplicity and cost-effectiveness of the Carter equation make it an appealing option for widespread application in body composition assessment. Summing up, the rationale for choosing the Carter equation over other models lies in its empirical validation, adaptability across populations, and practical application. Its ability to provide a reliable estimate of body fat percentage while being both accessible and cost-effective positions it as a preferred choice for clinical and research use alike.

The fat mass (FM) in kilograms was calculated by dividing the body fat percentage (BF%) by 100 and then multiplying it by the athlete’s total body weight (kg). The fat-free mass (FFM) in kilograms (kg) was then calculated by subtracting the FM (kg) from the total body mass. During the anthropometric measurements, the participant wore only a swimsuit, with no shoes or objects on his hands or head.

The anthropometric measurements were performed twice, in accordance with the guidelines of the International Society for the Advancement in Kinanthropometry (ISAK) by a level 1 certified anthropometrist, considering the corresponding intrapersonal technical error of measurement (TEM): 5% for skinfolds and 1% for other measurements [27].

It used the following formula for TEM and was calculated for each variable (

Table 2. Technical error of measurement.

Variables	TEM
Weight (kg)	0.24
Body fat (%)	0.15
Skinfolds (mm)	2.20

):

TEM = SD × √CV%/100

TEM values provide a measure of variability due to measurement error. Lower TEM values indicate higher reliability. For the data set provided, TEMs are within normal limits for anthropometric assessments at the professional level.

During the measurement process, the subject maintains a relaxed posture with their feet slightly apart and their arms comfortably to the side. The subject must put their feet together for certain measurements. In order to take measures as fast and effectively as feasible, the subject should be instructed to wear as little clothing as possible. For all measurement sites, swimming suits are ideal, but the anthropometrist should take into account the subject’s cultural beliefs and other practices. This is not the case with our subject. A flexible steel tape, measuring centimeters with millimeter gradations, is recommended for measuring girths.

Eight skinfolds are measured: biceps, triceps, subscapular, suprailiac, supraspinal, abdominal, front thigh, and calf.

The equipment used included a weighing scale (Omron BF511, Kyoto, Japan), a stable stadiometer (SECA 217, Hamburg, Germany), and a Harpenden Professional Skinfold Caliper (Baty International Ltd., Burgess Hill, UK) to measure the skinfolds. The measured skinfolds included biceps, subscapular, triceps, iliac crest, supraspinal, abdominal, thigh and calf [28]. A detailed nutritional plan was developed before each weekly microcycle, specifying the exact composition of meals and servings (type, amount, and timing of ingestions in relation to training sessions and type of beverages and/or other meal choices).

The plan was individually adjusted according to the results of the resting metabolic rate assessment, the recommended intake of macronutrients for swimming to match energy needs [10], the training volume and intensity of the sessions [29], and based on the swimmer’s feedback regarding the rate of perceived exertion (RPE) and subjective feelings after sessions [2].

As the energy demands of a 1500 m freestyle swimmer are increased due to high training volumes and the long duration of the sessions, foods and sports drinks were also consumed during pool workouts in order to help with the development of future nutritional plans. Individual food intake and the intake provided during pool sessions were primarily aimed to familiarize the athlete with the increased recommended carbohydrate amounts and to minimize the gastrointestinal distress associated with high carbohydrate intake [30]. The intake during high-intensity workouts and high-volume sessions consisted of 30–60 g CHO/h, provided from sports drinks [31].

The energy and macronutrient intake were adjusted according to low-, moderate-, and high-volume and intensity days (

Table 3. Specific macronutrient recommendations for each training intensity level.

Training Intensity Level	Carbohydrates (g/kg/Day)	Proteins (g/kg/Day)	Fats (% of Total Energy)
Low (Recovery or Technique Sessions)	3–5	1.2–1.4	20–30%
Moderate (Endurance Training)	5–7	1.4–1.6	20–30%
High (Threshold or Interval Training)	6–10	1.6–1.8	20–25%
Very High (Competition or Peak Training)	8–12	1.8–2.2	20–25%

). Therefore, the periodization of carbohydrates ranged between 4 and 12 g/bodyweight/day during the general and specific preparation phases. However, due to unexpected interruptions within the normal schedule, including the athlete’s inability to participate in some of the training sessions due to colds or respiratory tract infections, changes in the daily feeding program had to be established. Moreover, considering the social aspects of life and the challenges of adhering strictly to a dietary plan, even with flexible and varied weekly approaches, the athlete reported that he was not always able to strictly follow the discussed instructions. Therefore, these facts might be considered limitations of this case study.

During competition phases, especially for events held at the national level, feeding strategies were provided at the venues where the athlete was accommodated. Clear instructions were also given to the kitchen staff and hotel chefs in order to make sure that the meals were served in accordance with the recommended amounts, macronutrient intake, and cooking methods. Since most venues could provide only main meals for the athlete, he was instructed to prepare and consume additional snacks to ensure appropriate energy, nutrient, and fluid distribution during competition days. These snacks included foods specifically designed to be consumed before, between, and after events. The daily food intake was periodized according to the competition schedule, considering the number of events the athlete was due to compete in, the number of events per session, warm-up times, the time to warm up until the start of the event, the rest period between same- session events, the rest period until the next day’s events, or days without any events (including only warm-up sessions). These situations and national competitions provided essential information in order to develop appropriate feeding practices and habits for the athlete, for future feeding strategies during the upcoming elite-level international competitions. Moreover, trialing nutritional strategies and monitoring the food intake during local competitions ensured that the athlete could achieve predetermined levels of carbohydrates, which are known to enhance performance.

On the other hand, another limitation of this case study might be the inability of support staff to travel during international competitions. However, the swimmer was instructed to adhere to his usual eating habits and patterns wherever possible. Most venues hosting international elite competitions provide buffet-style meals with several food choices for the main meals [32]. Despite the lack of direct monitoring, the athlete received telephonic advice and guidance from the performance nutritionist regarding the most appropriate and available food choices in order to follow familiar eating strategies. The athlete was also asked to utilize the food photography method, sending photographs of his food selections to the sports nutritionist [33]. During this stage, the athlete was also entrusted to prepare additional provisions in advance, including familiar foods, snacks, and sports-specific nutrition products designed to be carried during travel. Nevertheless, the telephonically adapted nutritional interventions were reviewed at the end of each day—regardless of whether it was an event day or not—to verify and review with the athlete for potential changes.

3. Results

The outputs of the resting metabolic rate (RMR) measurements were recorded using the Omnia 1.6.8 software (Rome, Italy) via the information gathered from the exchange respiratory gases of the athlete. Anthropometric data were collected using the ISAK Restricted Proforma (Microsoft Excel, 2022), while the total energy and macronutrient intake were planned using the SENPRO (2023) dietary software. Besides anthropometric values and RMR data, the total energy intake and the following nutrients were carefully monitored: carbohydrates (CHO), proteins (PRO), and fats (FAT), including g/kg/bodyweight/day as well as total grams per day. Descriptive statistics were employed to explore the collected data.

3.1. Anthropometric, Body Composition Parameters, and Resting Metabolic Rate Values

Collected data of anthropometric measurements, body composition, and resting metabolic rate values are displayed in Table 3. The mean body weight of the athlete is 79.26 ± 1.72 kg, with a median value of 80 kg. The mean value of the BF% is 7.75%, meaning that it has minimal fluctuation, with a standard deviation of ±0.57%. However, the athlete changed his body composition in terms of fat-free mass (kg), as the mean value was 73.12 ± 1.30 kg, with the lowest value at 70.61 kg and the maximum value at 74.50 kg. The fat mass of the athlete remained relatively stable, with a mean value of 6.14 ± 0.56 kg. The mean resting metabolic value was 2905.75 ± 407.99 kcal/day, with a median of 3078 kcal/day. Moreover, this parameter also recorded the highest variation in terms of range, with minimum values of 2328 kcal/day and maximum values of 3461 kcal/day. The fluctuations in RMR values are further detailed in

Table 4. Status of anthropometric, body composition, and RMR values.

	Weight (kg)	Body Fat (%)	FM (kg)	FFM (kg)	Sum of 8 Skinfolds (mm)	RMR (kcal/Day)
Mean	79.26	7.75	6.14	73.12	61.46	2905.75
Standard Error	0.48	0.16	0.15	0.36	1.85	144.25
Median	80.00	7.80	6.20	73.92	62.00	3078.00
Standard Dev.	1.72	0.57	0.56	1.30	6.66	407.99
Range	5.20	1.90	1.67	3.89	22.00	1133.00
Minimum	76.00	6.90	5.31	70.61	52.00	2328.00
Maximum	81.20	8.80	6.98	74.50	74.00	3461.00
Confidence Level (95.0%)	1.04	0.35	0.34	0.79	4.03	341.09
CV	2%	7%	9%	2%	11%	14%

and Figure 1.

3.2. Dietary and Nutrient Intakes During the Macrocycle

The total dietary intake of the athlete fluctuated significantly during the macrocycle, based on the activity levels.

Table 5. Energy intake of the athlete prior to the Olympic qualification.

	Rest Day	1x Training Session/Day	2x Training Sessions/Day
Mean	2910.294	3805.979	4238.619
Standard Error	134.1226	137.7091	198.927
Median	3021.75	3786.75	4092.75
Standard Deviation	379.356	389.5002	562.6505
Range	1012.4	1272.72	1818.8
Minimum	2467.6	3327.28	3741.2
Maximum	3480	4600	5560
Confidence Level (95.0%)	317.1495	325.6303	470.3876
CV	13%	10%	13%

details the total energy intake of the swimmer on rest days, without physical activity, days with a single swimming session, and days with two swimming workouts. The mean energy intake during rest days was 2910 ± 379 kcal/day, which is in line with the measured resting metabolic rate values. On days with one training session, the mean energy intake was 3805 ± 389 kcal/day, while on days with two swimming sessions, it increased to 4238 ± 562 kcal/day. These findings are consistent with the anticipated rise in physiological needs, as physical activity intensifies and becomes more demanding.

The total energy intake of the athlete has consistently increased in response to the higher training volume, which is a normal adaptation, considering the higher energy demands of more frequent, longer, and/or more intense swimming training sessions [2]. Thus, the findings regarding the daily energy intake of the individual support the energy requirements of the athlete during sessions, as swimming workouts are expected to record energy demands of ~10 kcal/kg/h [34]. The data highlight that the proposed energy intake increases with the training volume, as notable variations in energy consumption were observed across the days with differing training loads. An overview of macronutrient intake is provided in Table 4, detailing the daily consumption of carbohydrates, proteins, and fats. These data allow for an analysis of macronutrient distribution across days with varying training loads.

As training for longer distance events in swimming requires extended sessions due to the increased volume of swimming blocks, muscle glycogen stores will be depleted [10], and, therefore, daily carbohydrate intake should be periodized to satisfy the increased fuel demands and ensure optimal performance during key sessions. Therefore, considering that moderate-to-high-intensity training sessions require a daily carbohydrates intake of 6–8 g/kg/day, our findings align with the literature [2], with the athlete’s mean intake reaching 568.99 ± 63.35 g of CHO per day. Moreover, when several swimming sessions took place in the same day, the daily CHO requirements increased to support these more demanding sessions, with mean intake values of 668.13 ± 119.32 g. These findings were expected, since the physiological requirements of training are higher when multiple or high-volume sessions are performed. (

Table 6. Nutrient intake of the athlete.

	CHO Intake (g/Day)			PRO Intake (g/Day)			Fat Intake (g/Day)
	Rest Day	1x Training Sessions/Day	2x Training Sessions/Day	Rest Day	1x Training Sessions/Day	2x Training Sessions/Day	Rest Day	1x Training Sessions/Day	2x Training Sessions/Day
Mean	296.44	568.99	668.13	197.88	152.63	158.28	103.67	103.67	103.67
Standard Error	19.52	22.40	42.18	1.55	5.94	1.23	6.92	6.92	6.92
Median	306.00	556.85	636.40	199.50	153.00	159.60	114.75	114.75	114.75
Standard Deviation	55.22	63.35	119.32	4.38	16.79	3.49	19.56	19.56	19.56
Range	161.20	202.60	363.00	12.25	49.68	9.80	41.15	41.15	41.15
Minimum	238.80	517.40	597.00	190.00	135.32	152.00	79.60	79.60	79.60
Maximum	400.00	720.00	960.00	202.25	185.00	161.80	120.75	120.75	120.75
Confidence Level (95.0%)	46.17	52.96	99.75	3.66	14.04	2.92	16.35	16.35	16.35
CV	19%	11%	18%	2%	11%	2%	19%	19%	19%

).

Regarding the highest variability in carbohydrate (CHO) intake, this was observed during the rest days of the athlete, when a lower amount of CHO was provided. This is evident from the coefficient of variation of 19%. The mean daily CHO intake during rest days was 296.44 ± 63.35 g, which still reflects the increased energy needs required to support the refueling and recovery process of the athlete, even on days without physical activity [35].

Dietary protein intake was prescribed to the athlete with the aim of optimizing protein synthesis and facilitating the recovery process from exercise while promoting the mitochondrial response to endurance and interval training sessions [2]. Therefore, the protein sources within the feeding plan of the athlete mainly consisted of foods with high-quality protein. Considering the protein intake provided to the athlete, the mean values for days with physical activity were 152.63 ± 16.79 g per day for the days with only one training session planned and 158.28 ± 3.49 g per day in the days with two swimming workouts. These values align with the recommended intake of 1.2–2 g/kg/day [2], underscoring the importance of increased protein intake in order to sustain the strength requirements of swimming. Due to the increased energy requirements of the athlete and the need to practically achieve an energy intake of at least 2910 ± 379 kcal/day during rest days, the protein intake was greater (197.88 ± 4.38 g/day) compared to days with scheduled training sessions. The approach was fundamentally supported by the need to reach the increased energy requirements of the athlete in order to prevent muscle catabolism and lean muscle mass loss associated with an unintentionally induced energy deficit due to the athlete’s elevated energy demands [36]. Another reason why the protein intake during training days (152.63 ± 16.79 g and 158.28 ± 3.49 g, respectively) was lower compared with rest days (197.88 ± 4.38 g/day) was attributed to protein’s stronger impact on satiety levels compared to other macronutrients, facilitating a reduction in energy provision from food [11], which in the end could hinder the athlete’s ability to obtain an optimal amount of energy from carbohydrates for physical activity [2].

Within the studied case, mean dietary fat intake (103.67 ± 19.56 g/day) remained consistent across all activity levels. This was due to the increased resting energy expenditure of the swimmer and the need to constantly satisfy energy needs, in order to promote optimal recovery, as fats provide 9 kcal/g and are an efficient energy source for achieving higher caloric intake [37]. In this particular case, the fat content within the diet was developed once the carbohydrate and protein needs were established, in line with the recommendations by Dominguez [2]. Moreover, fats served as an important energy source which fuel aerobic activities [38]. Given the long-distance training sessions characterized by high-volume and low-intensity workouts, the fat intake seemed appropriate for maximizing the contribution of fatty acids during exercise [2].

Overall, the implemented nutrition strategy effectively compensated for the substantial energy demands of the athlete, supporting the swimmer to achieve an increased energy availability without significant changes in body weight (79.26 ± 1.72 kg) during his preparation for the 2024 Paris Olympic Games. This evidence-based nutrition strategy highlighted the efforts in terms of fueling an elite-level athlete to achieve the required energy intakes in order to sustain swimming performance and challenges regarding food and serving ingestions to cover the high amounts of nutrients. To our best knowledge, these findings align with the very few studies on swimmers’ food intake across several macrocycle phases. Therefore, practical applications might be scarce and difficult to achieve [39]. In this regard, adjusting and testing the nutritional strategies during training sessions and national competitions, and matching them with the measured resting energy expenditure values, allowed the athlete to better understand and identify the most appropriate choices in terms of food choices, needed energy intake, nutrient amounts, and timing. The swimmer’s successful achievement of the Olympic Qualifying Time (FINA, 2022) underscores the importance of careful and detailed planning and implementation of a specific, scientifically grounded nutrition strategy.

The correlations between the main variables are shown in

Table 7. Pearson’s correlations.

Pearson’s r	Ant Data: Weight (kg)	Ant Data: Body Fat (%)	Ant Data: FM (kg)	Ant Data: FFM (kg)	Ant Data: Sum of 8 Skinfolds (mm)	MetaB: RMR (kcal/Day)	MetaB: VO2 Max (mL/min−1/kg−1)	MetaBVCO2 (L/min)	MetaB: RQ	S.U. at R.: FAT	S.U. at R.: FAT	S.U. at R.: CHO	S.U. at R.: CHO
Ant data: Weight	-	0.64	0.76	0.96	0.57	−0.39	−0.34	−0.48	−0.24	0.34	0.27	−0.34	−0.44
Ant data: Body fat	0.64	-	0.98	0.41	0.99	−0.29	−0.18	−0.60	−0.58	0.67	0.64	−0.67	−0.75
Ant data: FM	0.76	0.98	-	0.56	0.96	−0.33	−0.23	−0/445.61	−0.55	0.64	0.60	−0.64	−0.73
Ant data: FFM	0.96	0.41	0.56	-	0.32	−0.37	−0.34	−0.36	−0.08	0.17	0.09	−0.17	−0.26
Ant data: Sum of 8 skinfolds	0.57	0.99	0.96	0.32	-	−0.26	−0.15	−0.60	−0.64	0.71	0.69	−0.71	−0.79
MetaB: RMR	−0.39	−0.29	−0.33	−0.37	−0.26	-	0.99	0.83	0.04	−0.13	0.17	0.13	0.29
MetaB: VO2 max	−0.34	−0.18	−0.23	−0.34	−0.15	0.99	-	0.74	−0.11	0.02	0.32	−0.02	0.14
MetaB: VCO2	−0.48	−0.60	−0.61	−0.36	−0.60	0.83	0.74	-	0.59	−0.66	−0.40	0.66	0.77
MetaB: RQ	−0.24	−0.58	−0.55	−0.08	−0.64	0.04	−0.11	0.59	-	−0.99	−0.96	0.99	0.95
S.U. at R.: FAT	0.34	0.67	0.64	0.17	0.71	−0.13	0.02	−0.66	−0.99	-	0.95	−1.00	−0.98
S.U. at R.: FAT	0.27	0.64	0.60	0.09	0.69	0.17	0.32	−0.40	−0.96	0.95	-	−0.95	−0.89
S.U. at R.: CHO	−0.34	−0.67	−0.64	−0.17	−0.71	0.13	−0.02	0.66	0.99	−1.00	−0.95	-	0.98
S.U. at R.: CHO	−0.44	−0.75	−0.73	−0.26	−0.79	0.29	0.14	0.77	0.95	−0.98	−0.89	0.98	-

. Anthropometric data: weight and FFM (Free Fat Mass): a strong positive correlation of 0.963 indicates that as weight increases, the free fat mass also tends to increase.

Body fat and sum of eight skinfolds: a very strong positive correlation of 0.994. This indicates that as the body fat percentage increases, the sum of the eight skinfolds also generally increases.

Weight and body fat: a moderate positive correlation of 0.638 indicates that as weight increases, body fat also tends to increase, but the relationship is not as closely tied as the previous point.

Metabolic data: RMR (resting metabolic rate) and VO₂ max: a strong positive correlation of 0.988 indicates that individuals with higher resting metabolic rates tend to have higher VO₂ max values.

VO₂ max and VCO₂: a strong positive correlation of 0.736. This suggests that as VO₂ max (oxygen consumption) increases, the VCO₂ (carbon dioxide production) tends to increase as well.

Substrates used at Rest-RQ (respiratory quotient) and FAT: a very strong negative correlation of −0.990 suggests that as the RQ values increase, the use of fat as a substrate at rest typically decreases.

RQ and CHO (Carbohydrates): an equally strong positive correlation of 0.990. This indicates that as the RQ value increases, the use of carbohydrates as an energy substrate at rest usually increases.

Interplay between anthropometric and metabolic data: weight and RMR: a negative correlation of −0.393. This suggests that as weight increases, the resting metabolic rate tends to decrease, though the relationship is not very strong.

Body fat and VCO₂: a negative correlation of −0.595 indicates that as the body fat percentage increases, the VCO₂ tends to decrease.

Substrates used at rest and anthropometric data: FAT and body fat: a positive correlation of 0.667 suggests that as the use of fat as an energy substrate at rest increases, the body fat percentage also tends to increase.

CHO and the sum of the eight skinfolds: a strong negative correlation of −0.714. This means that as the use of carbohydrates as an energy substrate at rest goes up, the sum of the eight skinfolds tends to decrease.

4. Discussion

Anthropometric data: as one would expect, measures of body size and composition (like weight, body fat, and skinfold thickness) are strongly inter-related. For example, as weight increases, both the fat-free mass and the sum of skinfolds tend to increase.

Metabolic data: indicators of metabolism, such as the resting metabolic rate (RMR) and VO₂ max, are closely related. Those with a higher RMR tend to have a higher VO₂ max.

Substrates used at rest: the respiratory quotient (RQ) is an indicator of which fuel (fat or carbohydrate) is being predominantly metabolized. As the RQ increases, there is a shift from fat metabolism towards carbohydrate metabolism.

Interplay between different data types: there are relationships between body composition and metabolic measurements. For instance, as the body fat percentage increases, certain metabolic measures like VCO₂ decrease.

The data highlight the intricate relationships between body composition, metabolism, and the type of fuel our body uses at rest. However, it is crucial to remember that these are correlations, not causations, and the exact relationships can vary based on various factors, including genetics, diet, and activity levels.

Studies on elite athletes often highlight the importance of nutritional guidance and its impact on body composition and performance. For instance, nutritional interventions can lead to significant changes in body weight and composition, such as increased lean body mass and fat mass, depending on the energy balance and dietary intake [40]. Additionally, the body composition of athletes is crucial in weight-sensitive sports, where maintaining optimal body weight and composition is essential for performance [41].

The strengths of this study are represented by the fact that, during the study period, we had the possibility to better develop a nutritional strategy to match the athlete’s increased energy needs, based on periodic measurements of RMR undertaken with the athlete.

This study has some obvious limitations related to the design of this case study. During the RMR procedures, and due to the impossibility of controlling the room temperature, the seasonal differences between winter and summer might have influenced the athlete’s energy expenditure at rest [42]. Moreover, when training sessions had to be rescheduled or other commitments took priority, the proposed dietary intake had to be adjusted, and these changes may have prevented the athlete from fully adhering to the prescribed food plan, raising concerns that energy and nutrient intake might have been under-reported or underestimated [43]. Also, during international events, the athlete mostly travelled only with his coach. Psychological factors such as pressure, emotions, and nervousness, associated with competing at a high level, may have unintentionally limited his energy and food consumption [44,45]. To mitigate these challenges, nutritional strategies were trialed during local competitions. These trials aimed to help the athlete replicate effective practices during high-level events. Thus, pre-event meals and strategies were implemented to provide comfort for the swimmer, rather than rigid options focused solely on meeting the CHO intake guidelines [23]. Given these limitations, this study’s findings should be interpreted with caution, particularly in terms of transferability to other athletes and broader applications.

5. Conclusions

This case study focused on an elite-level male swimmer from the national team, currently the most successful athlete in the history of 800 m and 1500 m events in Romania. Even before the implementation of tailored nutritional strategies, the swimmer was already regarded as a promising talent and had established himself as the country’s top performer in these events at the start of this study.

In addition, according to the valuable interpretation of our results, we could trial and implement a scientific nutrition strategy during the season, including training sessions and national and international competitions. Matching the energy needs with the energy intake and effectively periodizing macronutrients and their amount within his diet represented a challenge due to the high amounts of foods and servings required to meet the athlete’s nutritional demands. This validates our methods for tracking body composition changes during the intervention, and it indicates a potential benefit of interventions aimed at boosting metabolic activity, such as tailored nutrition or exercise plans.

The conclusions of this study provide critical insights into how nutrition can be tailored to support the physiological demands of elite swimming. By understanding the interactions between the dietary intake, metabolic adaptations, and body composition, coaches and nutritionists can develop periodic nutrition strategies for different swimming events, such as sprints versus long-distance races. In this regard,

Table 8. Nutrition periodization according to the type of competition.

Competition Type	Effort Type	Nutritional Focus			Advice for Coaches
		Carbohydrates (g/Day)	Proteins (g/kg/Day)	Fats (%)
Sprint	Anaerobic	8–10	1.6–1.8	20–30	Schedule carbohydrate-rich meals 24–48 h before competitions. Incorporate fast-digesting protein sources post-training for rapid recovery.
Endurance	Aerobic	6–8	1.4–1.6	25–30	Train athletes to consume drinks/gels during training sessions and races. Focus on maintaining a balanced energy intake.

presents the practical implications of this study for coaches and nutritionists concerning sprint and endurance swimming events.

These strategies can be adapted for other aquatic sports and endurance disciplines (e.g., triathlon or rowing), highlighting the versatility of the periodized nutrition approach.

By integrating this information into training and nutrition practices, teams can ensure that swimmers are optimally prepared for the unique demands of their competitions, enhancing performance and recovery.

New Directions of Research

This case study opens up several directions of interest for future research that could improve our understanding of the relationship between nutrition, metabolic rate, and body composition in the sport context. These directions include the following: extending the research to a larger sample of athletes from various disciplines (not just swimming) to validate the observations and identify patterns that can be generalized; investigating elite athletes in sports (individual or team) that involve different combinations of energy systems, such as athletics, cycling, or football; analyzing the evolution of the metabolic rate and nutritional requirements in extreme environments, such as high altitude, extreme temperatures, or competitions in salt water; investigating how competitive stress influences the dietary intake, metabolic rate, and body composition.

6. Limitations

This paper has several limitations that must be recognized in order to properly evaluate the conclusions and their applicability. Without a control or comparison group, it is difficult to directly link the observed changes to nutritional intervention or to exclude the influence of other factors (training, stress, and natural variations).

Also, observed changes in the resting metabolic rate (RMR) and body composition (BMI) may be significantly influenced by the volume and intensity of training, not only by the nutritional intervention. Competition or other external events may affect metabolism and food intake, but these are not detailed or quantified in this study.

This paper provides valuable insights into the relationship between nutrition, RMR, and body composition in a high-performance context, but its limitations emphasize the need for future larger and better controlled research. These could include larger longitudinal group studies, experimental designs with control groups, and the integration of additional variables to support the validity and applicability of the findings.