Training and Body Composition during Preparation for a 48-Hour Ultra-Marathon Race: A Case Study of a Master Athlete

though the acute effects of ultra-endurance exercise on body composition have been well studied, limited information exists about the chronic adaptations of body composition to ultra-endurance training. The aim of the present study was to examine the day-by-day variation of training and body composition of a master athlete during the preparation for a 48-hour ultra-marathon race. For all training sessions (n= 73) before the race, the running distance, duration, and pace were recorded, and body mass, body fat (BF), body water (%), visceral fat, fat-free mass (FFM), four circumferences (i.e., waist, upper arm, thigh and calf), and eight skinfolds (i.e., chest, mid-axilla, triceps, subscapular, abdomen, iliac crest, thigh and calf) were measured accordingly in a 53-year-old experienced ultra-endurance athlete (body mass 80.1 kg, body height 177 cm, body mass index 25.6 kg m 2 ). The main ndings of the present study were that (a) the training plan of the ultra-endurance master athlete followed a periodization pattern with regard to exercise intensity and training volume, which increased over time, (b) the body mass, BF, and FFM decreased largely during the rst 30 training sessions, and (c) the circumferences and skinfolds re ected the respective decrease in BF. The ndings of this

study were that (a) the training plan of the ultra-endurance master athlete followed a periodization pattern with regard to exercise intensity and training volume, which increased over time, (b) the body mass, BF, and FFM decreased largely during the rst 30 training sessions, and (c) the circumferences and skinfolds re ected the respective decrease in BF. The ndings of this case study provided useful information about the variation of training and body composition during the preparation for an ultra-marathon race in a male master ultra-marathoner. The preparation for an ultra-endurance race seems to induce pronounced changes in body mass and body composition. Keywords:bio-impedance analysis; exercise intensity; master athlete; ultra-endurance 1. Introduction Exercise is de ned as ultra-endurance when it lasts more than six hours [1]. Consequently, this long duration of exercise results in energy de cit (i.e., negative energy balance) typically expressed by a reduction in body mass, body fat (BF), and fat-free mass (FFM) [2]. It seems that low BF results from the chronic adaptation to ultra-endurance training that might explain its relationship with performance. For instance, race completion in 100 km [3] and 24-hour ultra-marathon running [4] negatively correlates with BF, i.e., the lower the BF, the faster the race time. Therefore, it would be of great interest to estimate the chronic adaptations of body composition to ultra-endurance training. Int. J. Environ. Res. Public Health2019,16, 903; doi:10.3390/ijerph16060903

Int. J. Environ. Res. Public Health2019,16, 903 2 of 11 So far, many studies have examined the acute responses of body composition to ultra-endurance exercise. The decrease in body mass during an ultra-endurance race might even be >5% [5]. Finishing a 24-hour ultra-marathon (122–208 km) [6] and a 230-km run [7] resulted in a decrease in body mass by 1.7% and 1.0–2.5%, respectively, whereas the Antarctic race (2–4 weeks) induced decreases in body mass, BF, and FFM [8]. Furthermore, a reduction in both BF and FFM has been observed in a 5-day run (338 km) [9]. Thus, it was suggested that the exercise-induced decrease in body mass re ected a corresponding decrease in both BF and FFM. Although the abovementioned studies have enhanced our understanding of the acute responses to ultra-endurance races, limited information (e.g., open water swimming) [10] exists about the chronic adaptations of body composition to ultra-endurance training. It should be highlighted that ultra-marathon runners apply a larger training volume and lower exercise intensity than marathon runners [11]. Considering the increased number of those participating in ultra-endurance races and the concomitant energy de cit in these races [12], it would be of great practical value for sport and exercise science practitioners working in this sport to be aware of the effects of training on body composition in ultra-endurance athletes. Therefore, the main aim of the present study was to examine the variation in body composition during the preparation period for an ultra-endurance race (48-hour ultra-marathon) of a master athlete, de ned as an athlete older than 35 years [13]. A secondary aim was to study the agreement of four (i.e., body mass index—BMI, skinfold thicknesses, circumferences, and bio-impedance analysis) commonly used assessment methods of body composition. It was hypothesized that body mass and BF would decrease during this period due to the increased exercise-induced caloric consumption, and all measures of body composition should record similar trends of changes in these body composition parameters. 2. Materials and Methods 2.1. Study Design To examine the variation of training characteristics and body composition during the preparation for a 48-hour ultra-marathon, a

hypothesized that body mass and BF would decrease during this period due to the increased exercise-induced caloric consumption, and all measures of body composition should record similar trends of changes in these body composition parameters. 2. Materials and Methods 2.1. Study Design To examine the variation of training characteristics and body composition during the preparation for a 48-hour ultra-marathon, a longitudinal case study design was applied in which an ultra-endurance athlete was monitored in each training session. The participant was experienced, injury-free and did not receive any medication during the study period. After having been informed about the bene ts and risks of this study, the participant provided written informed consent. The study design was in accordance with the Declaration of Helsinski and was approved by the local institutional review board EKOS (Ethikkommission Ostschweiz). All experimental procedures were conducted from October 2017 to January 2018, the month when the race was performed. 2.2. Participant We studied a 53-year-old male ultra-endurance athlete (body mass 80.1 kg, body height 177 cm, body mass index 25.6 kg m 2 ) who was practicing ultra-endurance training and participating annually in several 6-hour, 12-hour, and 24-hour ultra-marathons. Between 1998 and 2017, he had participated in ve 6-hour runs, fourteen 12-hour runs, and six 24-hour runs. He started in these runs before (spring) and after (autumn) his triathlon season as an Ironman triathlete during summer. In contrast to 2017 when he competed in three 24-hour ultra-marathons, his plan was to compete in 2018 in a 48-hour ultra-marathon. Compared to his previous preparations for the 24-hour ultra-marathons, he planned to adopt a similar training program, increasing only the weekly training volume to 110–150 km without changing the running speed during the last three months prior to the race. His training principle was very simple. During winter, it was not possible for him to cycle due to weather conditions (i.e., cold, ice) and, therefore, he was mainly running. Generally, he was traveling in the morning by train to work and then in the early evening running at home (~14 km). This kind of training was normal for his

race. His training principle was very simple. During winter, it was not possible for him to cycle due to weather conditions (i.e., cold, ice) and, therefore, he was mainly running. Generally, he was traveling in the morning by train to work and then in the early evening running at home (~14 km). This kind of training was normal for his winter training. When the race came nearer, he was running both ways in the morning and the evening

Int. J. Environ. Res. Public Health2019,16, 903 3 of 11 and was also performing training sessions on Saturday and Sunday. The length of his training lap during weekends was similar to his running distance to work. Training for this ultra-marathon started after a long holiday of several weeks without training in autumn after his last triathlon. The rst sessions were only ~7 km. His pre-race preparation started on Monday 16 October 2017 and lasted until Sunday 21 January 2018 before he went to the race. After Christmas 2017, he was running every day. A Garmin global positioning system smartwatch v½voactive™(Garmin, Olathe, KS, USA) was used to maintain a pace of 8–9 min km 1 corresponding to 7.0–7.5 km h 1 as a target running speed for the race. To express running speed in % maximum oxygen uptake (%VO2max), we can estimate from a case report where intensity in %VO2maxwas determined during laboratory testing. An ultra-triathlete competing in a Triple Iron ultra-triathlon (i.e., 11.4 km swimming, 540 km cycling, and 126.6 km running) completed 115 km running in 18:22 h:min corresponding to a mean running speed of ~6.3 km h 1 [14]. Heart rate during the run was ~120 bpm, equal to ~45–55 %VO2max. For the present case study, running at 8–9 min km 1 could be considered at an intensity of ~55–65 %VO2max. The preparation was performed for the 48-hour ultra-marathon race starting on 26 January 2018, in Athens, Greece, in the context of the 13th Festival Athens Ultramarathon, part of the International Association of Ultrarunners (IAU). The race was to take place in a at area close to the sea. 2.3. Equipment and Protocols All measurements were performed at the same time of the day (9–10 p.m.) to avoid the effect of diurnal variations of anthropometric characteristics and body composition. Measurements were taken about three hours after dinner and after voiding the urinary bladder. Body mass, BF, percent body water, visceral fat, and FFM were measured using the Tanita BC-545 Bioelectrical Impedance Scale (Tanita Corporation of America Inc., Arlington Heights, IL, USA). Body height was measured using a stadiometer

the effect of diurnal variations of anthropometric characteristics and body composition. Measurements were taken about three hours after dinner and after voiding the urinary bladder. Body mass, BF, percent body water, visceral fat, and FFM were measured using the Tanita BC-545 Bioelectrical Impedance Scale (Tanita Corporation of America Inc., Arlington Heights, IL, USA). Body height was measured using a stadiometer (Tanita HR 001 Portable Height Measure, Tanita Europe, Amsterdam, The Netherlands) to the nearest 1 cm. The BMI was calculated as body mass divided by the square of body height (i.e., kg m 2 ). The thicknesses of eight skinfolds (i.e., chest, midaxillary, triceps, subscapular, abdomen, suprailiac, thigh, and calf) were measured on the right side of the body by a skinfold caliper (GPM-Hautfaltenmessgerät, Siber & Hegner, Zurich, Switzerland) to the nearest 0.2 mm. The circumference of the waist, upper arm, thigh, and calf was measured using a non-elastic measuring (cm) tape (KaWe CE, Kirchner und Welhelm, Germany) to the nearest 0.1 cm. All anthropometric measurements were performed by the same experienced investigator following the protocol of the International Society for the Advancement of Kinanthropometry (ISAK). The reliability of the investigator regarding measuring skinfold thicknesses of ultra-runners under eld conditions has already been determined [15]. 2.4. Statistical Analysis All statistical analyses were carried out using GraphPad Prism version 7.0 (GraphPad Software, San Diego, CA, USA) and IBM SPSS v.23.0 (SPSS, Chicago, IL, USA). A non-linear (4th grade) regression analysis examined the variation of training and anthropometric characteristics across pre-race preparation and the coef cient of determination (R 2 ) was calculated to analyze the proportion of the variance in the training characteristics and body composition that was predictable from the training sessions. Pearson correlation coef cient r was used to examine the relationship among the different assessment methods of body composition. The magnitude of the correlations was evaluated as trivial (r< 0.10), small (0.10 r< 0.30), moderate (0.30 r< 0.50), large (0.50 r< 0.70), very large (0.70 r< 0.90), and perfect (r 0.90) [16]. The acceptable type I error was set atp< 0.05. 3. Results The running distance, time, and

examine the relationship among the different assessment methods of body composition. The magnitude of the correlations was evaluated as trivial (r< 0.10), small (0.10 r< 0.30), moderate (0.30 r< 0.50), large (0.50 r< 0.70), very large (0.70 r< 0.90), and perfect (r 0.90) [16]. The acceptable type I error was set atp< 0.05. 3. Results The running distance, time, and pace per training session are depicted in Figure. The running distance ranged from 6.7 km (8th training session, i.e., 46% of the longest training session) to 14.5 km (67th), the training duration from 0:51 h:min (6th, 8th, and 11th, i.e., 44% of the longest training session)

Int. J. Environ. Res. Public Health2019,16, 903 4 of 11 to 1:56 h:min (57th), and the pace from 7:03 min:s.km 1 (64th) to 8:27 min:s.km 1 (57th, i.e., 20% slower than the fastest training session). The rst eleven training sessions had a distance of ~7 km and the following had ~14 km with a small variation between the sessions. Correspondingly, the running time was also doubled in the following sessions compared to the rst eleven. The running pace reached a “nadir” when the distance shifted from ~7 km to ~14 km and thereafter increased progressively to reach a “peak” at the 50–60th training sessions, and in the last training session decreased again.Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 4 of 11 reached a “nadir” when the distance shifted from ~7 km to ~14 km and thereafter increased progressively to reach a “peak” at the 50–60 th training sessions, and in the last training session decreased again. Figure 1. Training characteristics (i.e., running distance, time, and speed per training session) during the preparation for a 48-hour ultra-marathon. Body mass, body fat, and FFM decreased continuously during the first 30 training sessions; then, body fat increased until the end of the preparation, whereas body mass and FFM reached a “peak” close to the 60 th training session and, thereafter, decreased (Figure 2). The analysis of circumferences either in absolute values (Figure 3) or in comparison with baseline values (Figure 4) showed similar trends: all four circumference sites decreased progressively in the first 30 training sessions, and thereafter, presented smaller changes. The sum of the eight skinfolds across time presented a similar trend as the circumferences, i.e., a progressive decrease during the first 30 training sessions and smaller changes in the last period before the race (Figure 5). Nevertheless, this trend was not global for all skinfolds, e.g., two skinfolds (chest and subscapular) did not vary by time. The relationship of BF with the other measures ranged from moderate to large, whereas BMI correlated largely to very largely with BF, skinfolds, and circumferences (Table 1). The Σ8 skinfolds

changes in the last period before the race (Figure 5). Nevertheless, this trend was not global for all skinfolds, e.g., two skinfolds (chest and subscapular) did not vary by time. The relationship of BF with the other measures ranged from moderate to large, whereas BMI correlated largely to very largely with BF, skinfolds, and circumferences (Table 1). The Σ8 skinfolds correlated largely to very largely with circumferences. Figure 2. Body composition (i.e., mass, body fat, body water, visceral fat, and fat-free mass) during the preparation for a 48-hour ultra-marathon. Figure 1. Training characteristics (i.e., running distance, time, and speed per training session) during the preparation for a 48-hour ultra-marathon. Body mass, body fat, and FFM decreased continuously during the rst 30 training sessions; then, body fat increased until the end of the preparation, whereas body mass and FFM reached a “peak” close to the 60th training session and, thereafter, decreased (Figure). The analysis of circumferences either in absolute values (Figure) or in comparison with baseline values (Figure) showed similar trends: all four circumference sites decreased progressively in the rst 30 training sessions, and thereafter, presented smaller changes. The sum of the eight skinfolds across time presented a similar trend as the circumferences, i.e., a progressive decrease during the rst 30 training sessions and smaller changes in the last period before the race (Figure). Nevertheless, this trend was not global for all skinfolds, e.g., two skinfolds (chest and subscapular) did not vary by time. The relationship of BF with the other measures ranged from moderate to large, whereas BMI correlated largely to very largely with BF, skinfolds, and circumferences (Table). The S8 skinfolds correlated largely to very largely with circumferences.Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 4 of 11 reached a “nadir” when the distance shifted from ~7 km to ~14 km and thereafter increased progressively to reach a “peak” at the 50–60 th training sessions, and in the last training session decreased again. Figure 1. Training characteristics (i.e., running distance, time, and speed per training session) during the preparation for a 48-hour ultra-marathon. Body mass,

4 of 11 reached a “nadir” when the distance shifted from ~7 km to ~14 km and thereafter increased progressively to reach a “peak” at the 50–60 th training sessions, and in the last training session decreased again. Figure 1. Training characteristics (i.e., running distance, time, and speed per training session) during the preparation for a 48-hour ultra-marathon. Body mass, body fat, and FFM decreased continuously during the first 30 training sessions; then, body fat increased until the end of the preparation, whereas body mass and FFM reached a “peak” close to the 60 th training session and, thereafter, decreased (Figure 2). The analysis of circumferences either in absolute values (Figure 3) or in comparison with baseline values (Figure 4) showed similar trends: all four circumference sites decreased progressively in the first 30 training sessions, and thereafter, presented smaller changes. The sum of the eight skinfolds across time presented a similar trend as the circumferences, i.e., a progressive decrease during the first 30 training sessions and smaller changes in the last period before the race (Figure 5). Nevertheless, this trend was not global for all skinfolds, e.g., two skinfolds (chest and subscapular) did not vary by time. The relationship of BF with the other measures ranged from moderate to large, whereas BMI correlated largely to very largely with BF, skinfolds, and circumferences (Table 1). The Σ8 skinfolds correlated largely to very largely with circumferences. Figure 2. Body composition (i.e., mass, body fat, body water, visceral fat, and fat-free mass) during the preparation for a 48-hour ultra-marathon. Figure 2. Body composition (i.e., mass, body fat, body water, visceral fat, and fat-free mass) during the preparation for a 48-hour ultra-marathon.

Int. J. Environ. Res. Public Health2019,16, 903 5 of 11Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 5 of 11 Figure 3. Waist, upper arm, thigh, and calf circumference in absolute values during the preparation for a 48-hour ultra-marathon run. Figure 4. Waist, upper arm, thigh, and calf circumference expressed as a percentage of their baseline values during the preparation for a 48-hour ultra-marathon run. Figure 3. Waist, upper arm, thigh, and calf circumference in absolute values during the preparation for a 48-hour ultra-marathon run.Int. J. Environ. Res. Public Health 2019, 16, x FOR PEER REVIEW 5 of 11 Figure 3. Waist, upper arm, thigh, and calf circumference in absolute values during the preparation for a 48-hour ultra-marathon run. Figure 4. Waist, upper arm, thigh, and calf circumference expressed as a percentage of their baseline values during the preparation for a 48-hour ultra-marathon run. Figure 4. Waist, upper arm, thigh, and calf circumference expressed as a percentage of their baseline values during the preparation for a 48-hour ultra-marathon run.

This study examines training and body composition changes in a master athlete preparing for a 48-hour ultra-marathon.