Handcycling is a sport for athletes with physical disabilities, especially lower-limb disabilities, including those with spinal cord injury (SCI) and lower-limb amputations. Since 2004 (Athens Paralympic Games), it has been one of the Paralympic Games paracycling events. There, and during the World Championships, athletes compete in an individual time trial (TT) up to a maximum distance of 30 km (30–40 minutes) and a Road Race (RR) up to a maximum distance of 80 km (1:30–2:15).
Monitoring and quantification of training is a very important part of the long-term development of an endurance athlete. Success at major events such as the Paralympic Games or World Championships requires progression in both the total amount and intensity of an athlete's training. Therefore, it is essential that improvements in the physiological abilities are gained to meet the demands of competition.
It is well documented that performance capacity can be profoundly influenced by training intensity distribution (TID) and total training load (TTL) during training (15). Thus, the main goal of athletes and coaches during a training period is to regulate the TID and TTL in the most strategically efficient way to achieve the best results when racing (10,11,17). Tønnessen et al. (17) described training variations and TID in the year before a gold medal endurance performance in 11 elite XC skiers and biathletes. These athletes completed 90% of their endurance training as low-intensity training (LIT), which is below the first lactate threshold. These results are in accordance with general conclusions of TID in elite athletes, which support a bipolar approach (90% LIT and 10% high-intensity training) of training in endurance sports such as running, cycling, triathlon, and rowing (3,8,10,14,15).
Scientific literature including an accurate documentation of training loads and the quality (intensities) in Paralympic sports, especially in professional handcycling, is somewhat limited. Abel et al. (1,2) analyzed training and racing of a paraplegic handcyclist who competed in an ultraendurance (540 km) race and diagnosed performance (ventilatory and metabolic parameters) during a marathon competition. Janssen et al. (6) determined physical capacity, gross efficiency, and physical strain during a 10-km race of 16 male handcyclists (paraplegic and tetraplegic). However, to date, there are no analyses of training of a professional handcyclist.
The aim of this study was therefore to analyze the TTL and TID in handcycling for the first time during a full competitive season of a multiple female Paralympic medalist and comparing the results with other Paralympic and Olympic sports. The research study focuses on the preparation and competitive periods of the 2015 season (November 2014 to the end of September 2015; Total 45 weeks) as the athletes targets the 2016 Paralympic Games. The athlete's objectives for this season were to be able to compete at an international level over a sustained period. This approach would contribute toward securing national team slots for the Paralympic Games and increase the likelihood of being personally selected for the Rio 2016 Paralympic Games. The long-term outcome was to ultimately increase the physiological capacity of the athlete through the close monitoring of TTL and TID.
Experimental Approach to the Problem
The observation period began in November 2014, with a female handcyclist aged 54 years (lesion level: L2–3 incomplete, ASIA C, classification: WH5, height: 1.78 m, body mass: 65.0 kg). As a member of the German paracycling national team, she won 4 medals in the 2008 (2× bronze) and 2012 (1× bronze, 1× silver) Paralympic Games. In 2011, she was world champion in the RR.
Before any testing, all procedures received institutional ethical approval (in accordance with the Helsinki Declaration). Furthermore, after having had the study's objectives and all the procedures explained to her, the athlete provided written informed consent.
The athlete completed 2 laboratory-based (November 2014 and May 2015; Cologne) tests during the observation. In April 2014, the same test was conducted as well. The handcycling incremental stage tests were conducted using the athlete's hand cycle (Race Handbike; Schmicking, Holzwickede, Germany), which was mounted on a fully calibrated and validated ergometer (TE: 2%; Cyclus2; RBM Electronics, Leipzig, Germany) (12). The initial power output was set at 20 W and increased by 20 W every 5 minutes until exhaustion (1,18). At the end of every level, the blood lactate concentration (Biosen C-Line; EKF-Diagnostics, Magdeburg, Germany) was measured. These tests enabled the researchers, using nonlinear interpolation methods (5), to identify the power output corresponding to the lactate concentrations of the 3-zone intensity scale.
The athlete's personal training intensity was categorized according to the 3-zone intensity scale, which is used in well-trained to elite endurance athletes in many sports (15): zone 1: below blood lactate levels of 2 mmol·L−1; zone 2: between 2 mmol·L−1 and 4 mmol·L−1; and zone 3: above 4 mmol·L−1. According to Lucia et al., (9) time (in minutes) in each training intensity zone (heart rate [HR]-based time) was multiplied by the weighting factors 1, 2, and 3, respectively, to calculate the training load for each training session and to sum up the TTL per week. Calculation of TID and TTL from previously published data was performed using self-reporting (SR) and HR measurement (15). These values were compared with the results of this study, using power values (time in zone) of the SRM PowerMeter (Schoberer Rad Messtechnik, Jülich, Germany) (see above).
Training was prescribed by a member of the German Sports University Cologne. The training protocol was based on the 3-zone intensity scale according to Seiler (14). Examples of a typical training week in each training period (preparation, precompetition, and competition) are demonstrated in Table 1. Using an SRM PowerMeter (Schoberer Rad Messtechnik, Jülich, Germany), every training session was recorded, stored, and analyzed using the SRM Software (v3.1.408; Schoberer Rad Messtechnik) from the beginning of November 2014 to the end of September 2015. Time spent during the weight-training session is not included in the overall training time, as the focus of this work is on endurance training.
Training periods were set as the preparation (weeks 1–16; beginning of the observation until the first training camp), precompetition (weeks 17–28; first training camp until the first major race), and competition (weeks 29–45; including the national championships, world championships, and all world cups) periods.
Data are presented as mean ± SD and were analyzed using STATISTICA SOFTWARE 7.1 (StatSoft Europe GmbH, Hamburg, Germany). The TID was compared within the periods (mean for prep, precomp, and comp) of this study and with the results (mean for prep, precomp, and comp) of Lucía et al. (8) and Tønnessen et al. (17) using the chi-squared test (df = 2). The level of significance was set to α = 0.05 (*) and 0.01 (**).
The athlete completed 194 handcycling training sessions in a time of 433:53 hours. She covered a distance of 10,190.97 km and climbed 39,411.0 m during the investigation. An average training week consisted of 9:38 ± 4:50 hours of training during 4.3 ± 1.5 training sessions, with the maximal week comprising 8 training sessions in 23:12 hours. The athlete completed 34 weight-training sessions in the first 21 training weeks in addition to the endurance training.
The intensity distribution according to the 3-zone intensity scale was 71.6 ± 14.9% (zone 1), 15.2 ± 8.0% (zone 2), and 13.1 ± 5.5% (zone 3). The TID during the different training periods is shown in Figure 1. The preparation period intensity differed significantly from that of the competition period in this study (p ≤ 0.05). During the preparation period, percentage of zone 1 was lower and zone 2 was higher in this study. The preparation period intensity in this study differ from that of Lucía et al. (8) (p < 0.01) and Tønnessen et al. (17) (p < 0.01). Both, Lucía et al. (8) and Tønnessen et al. (17) showed higher percentage of training in zone 1, whereas lower values in zone 2 and 3. During the precompetition period, the intensity of this study and that of Tønnessen et al. (17) differ significantly (p < 0.01). Here, the percentage of zone 1 was lower, whereas those of zone 2 and 3 were higher in this study. Figure 2 shows the course of the intensity distribution over all 45 weeks.
The accumulated mean training load throughout the season was 804 ± 399 (zone 1: 424 ± 252; zone 2: 166 ± 114; and zone 3: 215 ± 130). The weekly training load is shown in Figure 2. The TTL during the preparation was 579, during the precompetition period, it was 1,006, and during the competition period, it was 875 (Figure 3).
During the investigation, the performance capacity in the incremental exercise test analog to 4 mmol·L−1 increased by approximately 20% (150–181 W), whereas the peak power output (PPO) improved by 11% (180–200 W). Both improvements are greater than the smallest worthwhile change of 2.8/3.7 W (0.2 × SD). The metabolic parameters are summarized in Table 2. Figure 4 shows the results of the 3 incremental exercise tests, which were used to set the training zones.
To the authors' knowledge, this is the first study detailing the monitoring and quantification of the training completed during a full competitive season of a professional female handcyclist. Maintaining a high fitness level throughout the season was important to collect national qualification points for the 2016 Paralympic Games and to be personally nominated for the Games. Both aims were achieved by winning every world cup TT of the documented season.
The most effective way to increase performance and long-term development of successful athletes in endurance sports is associated with a bipolar TID (80% zone 1 and 20% zone 2/3) (3,14,15). The analysis of the TID throughout this investigation shows a tendency toward a greater volume in zone 2 than is recommended in the literature (3,14,15). This difference may demonstrate and include possible room for improvement for the upcoming Paralympic season (Rio 2016). Mujika et al. (10,11) showed higher volumes in zone 1 in both a Paralympic and an Olympic triathlete. The percentage (time spend in zone) in zone 3 of this study is comparable to that of Mujika et al. (10,11) As expected, competition weeks demonstrated higher values of zone 3 activities than weeks without racing. Comparing the TID in different training periods and authors (8,17), there is a significant difference of zone 1 training in the recent study.
The evolution of the TID in the recent study toward a more polarized model (15) in the competition period is comparable with the increase in training volume. The TID and training volume in the competition period are similar to the findings of Tønnessen et al. (17) who reported TID to remain constant over all training periods, but the training volumes to decrease over time. Lucía et al. (8) analyzed the training data of professional cyclists over a complete season, including preparation, precompetition, and competition periods. In the precompetition and competition periods, the TID did not differ significantly. Therefore, we conclude that a higher training volume results in a shift in the TID toward a higher percentage of zone 1 to increase TTL without risking overtraining.
Sylta et al. (16) analyzed the accuracy of SR training duration and intensity distribution, which was used by Tønnessen et al. (17) Self-reporting data were therefore compared with HR data. The extent to which the evaluation of SR, HR, and power is comparable to analyze the TTL and TID should be addressed in prospective research. This is supported by Stöggl and Sperlich (15), who conclude that a “nonuniform TID among endurance disciplines may arise from differences in methodology in retrospective analyses.” Regarding future research, we would suggest a similar study with a group of professional handcyclists (paraplegic and tetraplegic) should be conducted to compare TID using different methods (SR, HR, and power) with the goal of determining optimal training loads for improving performance capacity while avoiding overtraining/overuse injuries.
The mean TTL was 804 ± 399, which is higher than that previously reported by Mujika et al. for a male world champion paratriathlete (735) (11) and lower than that for a world-class female triathlete (1,537) (10). The high TTL displays the demands of competing at Olympic level—the athlete came in sixth in the 2012 Olympic Games. At the beginning of the observation of Mujika et al., (11) the paratriathlete trained on a recreational level, which could be the reason for the lower values.
The results of Mujika et al. (11) show a greater (73.4% vs. 59.7%) development of absolute power output at a lactate concentration of 4 mmol·L−1 in the incremental cycling tests. The analysis of the training load and evolution of the power output at 4 mmol·L−1 shows that the fitness and training level of the paratriathlete were initially lower than in our athlete, which left more potential for development. However, the results show a clear improvement from April 2014 to May 2015 in power according to 4 mmol·L−1 and PPO during a comparable period of training.
In comparison to Abel et al., (1) the amount of improvement in the performance capacity at 4 mmol·L−1 is less (63.8% vs. 19%), whereas the PPO in our study is higher overall (180 W vs. 200 W). Abel et al. explain the “extraordinary improvement” in the athlete with a reduced training level at the start of the intervention and the moderate training intensity levels. The last suggestion is somehow misleading while the intensity distribution according to the 3-zone model was approximately (calculated from the results of Abel et al.) 55%/25%/20%. Compared with our results, the percentage of zone 1 is lower and that of zones 2 and 3 higher. The results of Abel et al. are also in contrast to the literature in the area of able-bodied athletes in running, cycling, cross-country skiing, and rowing (14,15).
These differences in the TID of different sports require a closer look at the muscle fiber distribution of the upper and lower extremities to identify a possible cause. Schantz et al. (13) compared muscle fiber distribution in the anterior deltoid muscle in untrained able-bodied, paraplegic (untrained/trained), and tetraplegic (untrained/trained) subjects. In all SCI groups (except for trained paraplegics), the ratio of type I fibers was higher than that in the able-bodied group, whereas the proportion of type IIB fibers was lower. Although the number of type II muscle fibers recruited rises during higher intensity (7), a higher proportion of zone 2 and 3 training could be beneficial for triggering endurance adaptation mechanisms in the fast-twitch fiber types (4). Additional work has to be done in the comparison of endurance training of the upper and lower extremities, particularly considering different lesions and possible limitations of muscle fiber recruitment and in the cardiovascular system. Further research should address this topic, especially in paraplegic athletes and in tetraplegic athletes because the limitations are higher in this population.
Monitoring the TTL in handcycling is an important component in sports science consultation work and coaching. Training intensity distribution is a useful tool to compare training in different sports with results in scientific literature. Possible influences of different methods of measuring TID should be considered. However, further analysis of the TID in SCI athletes, especially paraplegic athletes, focusing on muscle fiber distribution should be addressed in future. Currently, no general guideline for the TTL and TID in paraplegic endurance athletes can be given.
The authors thank the athlete very much for her relentless hard work and willingness to share her data.
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