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Differences between Treadmill and Cycle Ergometer Cardiopulmonary Exercise Testing Results in Triathletes and Their Association with Body Composition and Body Mass Index
Cardiopulmonary exercise testing (CPET) is the method of choice to assess aerobic fitness. Previous research was ambiguous as to whether treadmill (TE) and cycle ergometry (CE) results are transferrable or different between testing modalities in triathletes. The aim of this paper was to investigate the differences in HR and VO2 at maximum exertion between TE and CE, at anaerobic threshold (AT) and respiratory compensation point (RCP) and evaluate their association with body fat (BF), fat-free mass (FFM) and body mass index (BMI). In total, 143 adult (n = 18 female), Caucasian triathletes had both Tr and CE CPET performed. The male group was divided into <40 years (n = 80) and >40 years (n = 45). Females were aged between 18 and 46 years. Body composition was measured with bioelectrical impedance before tests. Differences were evaluated using paired t-tests, and associations were evaluated in males using multiple linear regression (MLR). Significant differences were found in VO2 and HR at maximum exertion, at AT and at RCP between CE and TE testing, in both males and females. VO2AT was 38.8 (±4.6) mL/kg/min in TE vs. 32.8 (±5.4) in CE in males and 36.0 (±3.6) vs. 32.1 (±3.8) in females (p < 0.001). HRAT was 149 (±10) bpm in TE vs. 136 (±11) in CE in males and 156 (±7) vs. 146 (±11) in females (p < 0.001). VO2max was 52 (±6) mL/kg/min vs. 49 (±7) in CE in males and 45.3 (±4.9) in Tr vs. 43.9 (±5.2) in females (p < 0.001). HRmax was 183 (±10) bpm in TE vs. 177 (±10) in CE in males and 183 (±9) vs. 179 (±10) in females (p < 0.001). MLR showed that BMI, BF and FFM are significantly associated with differences in HR and VO2 at maximum, AT and RCP in males aged >40. Both tests should be used independently to achieve optimal fitness assessments and further training planning.
Journal Article
Training Session Models in Endurance Sports: A Norwegian Perspective on Best Practice Recommendations
Background: Our scientific understanding of the mechanistic and practical connections between training session prescriptions, their execution by athletes, and adaptations over time in elite endurance sports remains limited. These connections are fundamental to the art and science of coaching. Objective: By using successful Norwegian endurance coaches as key informants, the aim of this study is to describe and compare best practice session models across different exercise intensities in Olympic endurance sports. Methods: Data collection was based on a four-step pragmatic qualitative study design, involving questionnaires, training logs from successful athletes, and in-depth and semi-structured interviews, followed by negotiation among researchers and coaches to assure our interpretations. Twelve successful and experienced male Norwegian coaches from biathlon, cross-country skiing, long-distance running, road cycling, rowing, speed skating, swimming, and triathlon were chosen as key informants. They had been responsible for the training of world-class endurance athletes who altogether have won > 370 medals in international championships. Results: The duration of low-intensity training (LIT) sessions ranges from 30 min to 7 h across sports, mainly due to modality-specific constraints and load tolerance considerations. Cross-training accounts for a considerable part of LIT sessions in several sports. Moderate (MIT)- and high-intensity training (HIT) sessions are mainly conducted as intervals in specific modalities, but competitions also account for a large proportion of annual HIT in most sports. Interval sessions are characterized by a high accumulated volume, a progressive increase in intensity throughout the session, and a controlled, rather than exhaustive, execution approach. A clear trend towards shorter intervals and lower work: rest ratio with increasing intensity was observed. Overall, the analyzed sports implement considerably more MIT than HIT sessions across the annual cycle. Conclusions: This study provides novel insights on quantitative and qualitative aspects of training session models across intensities employed by successful athletes in Olympic endurance sports. The interval training sessions revealed in this study are generally more voluminous, more controlled, and less exhaustive than most previous recommendations outlined in research literature.
Journal Article
The Impact of Resistance Training on Swimming Performance: A Systematic Review
Background
The majority of propulsive forces in swimming are produced from the upper body, with strong correlations between upper body strength and sprint performance. There are significant gaps in the literature relating to the impact of resistance training on swimming performance, specifically the transfer to swimming performance.
Objective
The aims of this systematic literature review are to (1) explore the transfer of resistance-training modalities to swimming performance, and (2) examine the effects of resistance training on technical aspects of swimming.
Methods
Four online databases were searched with the following inclusion criteria: (1) journal articles with outcome measures related to swimming performance, and (2) competitive swimmers participating in a structured resistance-training programme. Exclusion criteria were (1) participants with a mean age <16 years; (2) untrained, novice, masters and paraplegic swimmers; (3) triathletes and waterpolo players; (4) swimmers with injuries or illness; and (5) studies of starts and turns specifically. Data were extracted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the Physiotherapy Evidence Database (PEDro) scale was applied.
Results
For optimal transfer, specific, low-volume, high-velocity/force resistance-training programmes are optimal. Stroke length is best achieved through resistance training with low repetitions at a high velocity/force. Resisted swims are the most appropriate training modality for improving stroke rate.
Conclusion
Future research is needed with respect to the effects of long-term resistance-training interventions on both technical parameters of swimming and overall swimming performance. The results of such work will be highly informative for the scientific community, coaches and athletes.
Journal Article
Predicting daily recovery during long-term endurance training using machine learning analysis
PurposeThe aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures.MethodsSelf-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model.ResultsPrediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5–23.6 and 0.05–0.44 for AM PRS and HRV change, respectively).ConclusionAt the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy.
Journal Article